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Introduction to Data Preprocessing in Machine Learning

The following article provides an outline for Data Preprocessing in Machine Learning. Data pre-processing also knows as data wrangling is the technique of transforming the raw data i.e. an incomplete, inconsistent, data with lots of error, and data that lack certain behavior, into understandable format carefully using the different steps (i.e. from importing libraries, data to checking of missing values, categorical followed by validation and feature scaling ) so that proper interpretations can be made from it and negative results can be avoided, as the quality of the model in machine learning highly depends upon the quality of data we train it on.

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Data collected for training the model is from various sources. These collected data are generally in their raw format i.e. they can have noises like missing values, and relevant information, numbers in the string format, etc. or they can be unstructured. Data pre-processing increases the efficiency and accuracy of the machine learning models. As it helps in removing these noises from and dataset and giving meaning to the dataset

Six Different Steps Involved in Machine Learning

Following are six different steps involved in machine learning to perform data pre-processing:

Step 1: Import libraries

Step 2: Import data

Step 3: Checking for missing values

Step 4: Checking for categorical data

Step 5: Feature scaling

1. Import Libraries

The very first step is to import a few of the important libraries required in data pre-processing. A library is a collection of modules that can be called and used. In python, we have a lot of libraries that are helpful in data pre-processing.

A few of the following important libraries in python are:

Numpy: Mostly used the library for implementing or using complicated mathematical computation of machine learning. It is useful in performing an operation on multidimensional arrays.

Pandas: It is an open-source library that provides high performance, and easy-to-use data structure and data analysis tools in python. It is designed in a way to make working with relation and labeled data easy and intuitive.

Matplotlib: It’s a visualization library provided by python for 2D plots o array. It is built on a numpy array and designed to work with a broader Scipy stack. Visualization of datasets is helpful in the scenario where large data is available. Plots available in matplot lib are line, bar, scatter, histogram, etc.

Seaborn: It is also a visualization library given by python. It provides a high-level interface for drawing attractive and informative statistical graphs.

2. Import Dataset

Once the libraries are imported, our next step is to load the collected data. Pandas library is used to import these datasets. Mostly the datasets are available in CSV formats as they are low in size which makes it fast for processing. So, to load a csv file using the read_csv function of the panda’s library. Various other formats of the dataset that can be seen are

Once the dataset is loaded, we have to inspect it and look for any noise. To do so we have to create a feature matrix X and an observation vector Y with respect to X.

3. Checking for Missing Values

Once you create the feature matrix you might find there are some missing values. If we won’t handle it then it may cause a problem at the time of training.

Removing the entire row that contains the missing value, but there can be a possibility that you may end up losing some vital information. This can be a good approach if the size of the dataset is large.

If a numerical column has a missing value then you can estimate the value by taking the mean, median, mode, etc.

4. Checking for Categorical Data

Data in the dataset has to be in a numerical form so as to perform computation on it. Since Machine learning models contain complex mathematical computation, we can’t feed them a non-numerical value. So, it is important to convert all the text values into numerical values. LabelEncoder() class of learned is used to covert these categorical values into numerical values.

5. Feature Scaling

The values of the raw data vary extremely and it may result in biased training of the model or may end up increasing the computational cost. So it is important to normalize them. Feature scaling is a technique that is used to bring the data value in a shorter range.

Methods used for feature scaling are:

 Rescaling (min-max normalization)

 Mean normalization

 Standardization (Z-score Normalization)

 Scaling to unit length

6. Splitting Data into Training, Validation and Evaluation Sets

Finally, we need to split our data into three different sets, training set to train the model, validation set to validate the accuracy of our model and finally test set to test the performance of our model on generic data. Before splitting the Dataset, it is important to shuffle the Dataset to avoid any biases. An ideal proportion to divide the Dataset is 60:20:20 i.e. 60% as the training set, 20% as test and validation set. To split the Dataset use train_test_split of sklearn.model_selection twice. Once to split the dataset into train and validation set and then to split the remaining train dataset into train and test set.

Conclusion – Data Preprocessing in Machine Learning

Data Preprocessing is something that requires practice. It is not like a simple data structure in which you learn and apply directly to solve a problem. To get good knowledge on how to clean a Dataset or how to visualize your dataset, you need to work with different datasets. The more you will use these techniques the better understanding you will get about it. This was a general idea of how data processing plays an important role in machine learning. Along with that, we have also seen the steps needed for data pre-processing. So next time before going to train the model using the collected data be sure to apply data pre-processing.

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Machine Learning With Limited Data

This article was published as a part of the Data Science Blogathon.

Introduction

In machine learning, the data’s mount and quality are necessary to model training and performance. The amount of data affects machine learning and deep learning algorithms a lot. Most of the algorithm’s behaviors change if the amount of data is increased or decreased. But in the case of limited data, it is necessary to effectively handle machine learning algorithms to get better results and accurate models. Deep learning algorithms are also data-hungry, requiring a large amount of data for better accuracy.

In this article, we will discuss the relationship between the amount and the quality of the data with machine learning and deep learning algorithms, the problem with limited data, and the accuracy of dealing with it. Knowledge about these key concepts will help one understand the algorithm vs. data scenario and will shape one so that one can deal with limited data efficiently.

The “Amount of Data vs. Performace” Graph

In machine learning, a query could be raised to your mind, how strictly is the data required to train a good machine learning or deep learning model? Well, there is no threshold levels or fixed answer to this, as every piece of information is different and has different features and patterns. Still, there are some threshold levels after which the performance of the machine learning or deep learning algorithms tends to be constant.

Most of the time, machine learning and deep learning models tend to perform well as the amount of data fed is increased, but after some point or some amount of data, the behavior of the models becomes constant, and it stops learning from data.

The above pictures show the performance of some famous machine learning and deep learning architectures with the amount of data fed to the algorithms. Here we can see that the traditional machine learning algorithms learn a lot from the data in a preliminary period, where the amount of data fed is increasing, but after some time, when a threshold level comes, the performance becomes constant. Now, if you provide more data to the algorithm, it will not learn anything, and the version will not increase or decrease.

In the case of deep learning algorithms, there are a total of three types of deep learning architectures in the diagram. The shallow ty[e of deep learning stricture is a minor deep learning architecture in terms of depth, meaning that there are few hidden layers and neurons in external deep learning architectures. In the case o deep neural networks, the number of hidden layers and neurons is very high and designed very profoundly.

From the diagram, we can see a total of three deep learning architectures, and all three perform differently when some amount of data is fed and increased. The shallow, deep neural networks tend to function like traditional machine learning algorithms, where the performance becomes constant after some threshold amount of data. At the same time, deep neural networks keep learning from the data when new data is fed.

From the diagram, we can conclude that,

” THE DEEP NEURAL NETWORKS ARE DATA HUNGRY “

What Problems Arise with Limited Data?

Several problems occur with limited data, and the model could perform better if trained with limited data. The common issues that arise with limited data are listed below:

1. Classification: 

In classification, if a low amount of data is fed, then the model will classify the observations wrongly, meaning that it will not give the accurate output class for given words.

2. Regression:

In a regression problem, if the model’s accuracy is low, then the model will predict very wrong, meaning that as it is a regression problem, it will be expecting the number. Still, limited data may show a horrifying amount far from the actual output.

3. Clustering:

The model can classify the different points in the wrong clusters in the clustering problems if trained with limited data.

4. Time Series:

In time series analysis, we forecast some data for the future. Still, a low-accurate time series model can give us inferior forecast results, and there may be a lot of errors related to time.

5. Object Detection:

If an object detection model is trained on limited data, it might not detect the object correctly, or it can classify the thing incorrectly.

How to Deal With Problems of Limited Data?

There needs to be an accurate or fixed method for dealing with the limited data. Every machine learning problem is different, and the way of solving the particular problem is other. But some standard techniques are helpful for many cases.

1. Data Augmentation

Data augmentation is the technique in which the existing data is used to generate new data. Here the further information generated will look like the old data, but some of the values and parameters would be different here.

This approach can increase the amount of data, and there is a high likelihood of improving the model’s performance.

Data augmentation is preferred in most deep-learning problems, where there is limited data with images.

2. Don’t Drop and Impute:

In some of the datasets, there is a high fraction of invalid data or empty. Due to that, some amount of data s dropped not to make the process complex, but by doing this, the amount of data is decreased, and several problems can occur.

3. Custom Approach:

If there is a case of limited data, one could search for the data on the internet and find similar data. Once this type of data is obtained, it can be used to generate more data or be merged with the existing data.

Conclusion

In this article, we discussed the limited data, the performance of several machine learning and deep learning algorithms, the amount of data increasing and decreasing, the type of problem that can occur due to limited data, and the common ways to deal with limited data. This article will help one understand the process of restricted data, its effects on performance, and how to handle it.

Some Key Takeaways from this article are:

1. Machine Learning and shallow neural networks are the algorithms that are not affected by the amount of data after some threshold level.

2. Deep neural networks are data-hungry algorithms that never stop learning from data.

3. Limited data can cause problems in every field of machine learning applications, e.g., classification, regression, time series, image processing, etc.

4. We can apply Data augmentation, imputation, and some other custom approaches based on domain knowledge to handle the limited data.

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Data Drift And Machine Learning Model Sustainability

1.   Background

In many real-world applications where machine learning models have been deployed in production, often the data evolve over time and thus models built for analyzing such data quickly become obsolete over time. It becomes essential for data scientists to monitor the model performance over time. Is the machine learning model deployed sustainable and performing consistently? Usually, the scenario that occurs over time, is not because the model stops performing well but simply because the model can no longer capture the right variability of the data be it the dependent or independent variables. The reason for this is not to do with the machine learning model itself, but the data distributions. There is a shift that occurs at the data level, as the distribution of the data used to train an ML model, called the source distribution, is different from the distribution of the newly available data, the target distribution. As a result of this, the relationships between input and output data can change over time, meaning that in turn there are changes to the unknown underlying mapping function. This gap is where the concept of data drift comes in.  

2.   What is Data Drift?

Over time, a machine learning model starts to lose its predictive power, a concept known as model drift. What is generally called data drift is a change in the distribution of data used in a predictive task. There are different types of data drift such as:  

Feature Drift (or covariate drift):

It happens when some previously infrequent or even unseen feature vectors become more frequent, and vice versa. However, the relationship between the feature and the target is still the same. [1] Covariate Drift is defined as the case where: and Example: Temperature readings changing from in degree Fahrenheit to degree Celsius.

Figure 1. Changes in the distribution of the feature “period” over time, yields to covariate shift  

Concept Drift:

It is the phenomenon where the statistical properties of the class variable — in other words, the target we want to predict — change over time. Hence, this drift is focussed on the change in the target variable with time. [1] Concept Drift is defined as the case where: and Example: Inventory changes over time

Figure 2. Concept drift can be detected by the divergence of model & data decision boundaries and the concomitant loss of predictive ability  

Dual Drift:

This applies to a situation wherein feature as well as concept drift occurs. [1] Dual Drift is defined as a case where:

3.   Methods for detection of Data Drift

Following are a number of techniques that have been explored in python using the ‘scikit-multiflow’ library. The multitude of techniques have been implemented by showing a simulation of how it will work on real life data.

Adaptive Windowing(ADWIN):

ADWIN [2] (ADaptive WINdowing) is an adaptive sliding window algorithm for detecting change, and keeping updated statistics about a data stream. ADWIN allows algorithms not adapted for drifting data, to be resistant to this phenomenon. The general idea is to keep statistics from a window of variable size while detecting concept drift. This algorithm is implemented in python for a specialized tool which can perform drift detection. The scikit-multiflow library has this functionality. A sample code to simulate concept drift and the way it can be detected using this library is as follows

The results of the simulation conducted to detect data drift look like:

Drift Detection Method (DDM):

DDM (Drift Detection Method) [3] is a concept change detection method based on the PAC learning model premise, that the learner’s error rate will decrease as the number of analysed samples increase, as long as the data distribution is stationary. If the algorithm detects an increase in the error rate, that surpasses a calculated threshold, either change is detected or the algorithm will warn the user that change may occur in the near future, which is called the warning zone. The results of the simulation conducted to detect data drift using the DDM are attached below:

Early Drift Detection Method (EDDM):

EDDM (Early Drift Detection Method) [4] aims to improve the detection rate of gradual concept drift in DDM, while keeping a good performance against abrupt concept drift. This method works by keeping track of the average distance between two errors instead of only the error rate. For this, it is necessary to keep track of the running average distance and the running standard deviation, as well as the maximum distance and the maximum standard deviation. The results of the simulation implemented to detect data drift using the DDM are attached below:

Drift Detection Method based on Hoeffding’s bounds with moving average-test (HDDM_A):

HDDM_A [5] is a drift detection method based on the Hoeffding’s inequality. HDDM_A uses the average as estimator. It receives as input a stream of real values and returns the estimated status of the stream: STABLE, WARNING or DRIFT. The results of the simulation conducted to detect data drift using the DDM are attached below:

Drift Detection Method based on Hoeffding’s bounds with moving weighted average-test (HDDM_W):

HDDM_W [5] is an online drift detection method based on McDiarmid’s bounds. HDDM_W uses the EWMA statistic as estimator. It receives as input a stream of real predictions and returns the estimated status of the stream: STABLE, WARNING or DRIFT. The results of the simulation conducted to detect data drift using the DDM are attached below:

Kolmogorov-Smirnov Windowing method for concept drift detection (KSWIN):

KSWIN (Kolmogorov-Smirnov Windowing) [6] is a concept change detection method based on the Kolmogorov-Smirnov (KS) statistical test. KS-test is a statistical test with no assumption of underlying data distribution. KSWIN can monitor data or performance distributions. Note that the detector accepts one dimensional input as array. The results of the simulation conducted to detect data drift using the DDM are attached below:

Page-Hinkley method for concept drift detection (PageHinkley):

This change detection method works by computing the observed values and their mean up to the current moment. Page-Hinkley won’t output warning zone warnings, only change detections. The method works by means of the Page-Hinkley test [7]. In general lines it will detect a concept drift if the observed mean at some instant is greater then a threshold value lambda. The results of the simulation conducted to detect data drift using the DDM are attached below:

4.   Metrics for measuring Data Drift

Different methods and metrics are used depending on the type of drift that needs to be monitored. The methods depending on the type of drift are as follows:

Measuring Concept Drift:

Following are the proposed quantitative measures of concept drift including the key measure drift magnitude which measures the distance between two concepts Pt(X,Y) and Pu(X,Y). Using the Hellinger distance method which measures the total variation distance: where Z represents a vector of random variables.  

Measuring Covariate Drift:

For the conditional drifts it is necessary to deal with multiple distributions, one for each value of the conditioning attributes. We address this by weighted averaging, as described:

For a given subset of the covariate attributes there will be a conditional probability distribution over the possible values of the covariate attributes for each specific class, y. The conditional marginal covariate drift is the weighted sum of the distances between each of these probability distributions from time period t to u, where the weights are the average probability of the class over the two time periods.

For each subset of the covariate attributes there will be a probability distribution over the class labels for each combination of values of those attributes, x ̄ at each time period. Therefore, the Conditional Class Drift can be calculated as the weighted sum of the distances between these probability distributions where the weights are the average probability over the two periods of the specific value for the covariate attribute subset.

5.   Model pipeline for overcoming data drift:

The following figure reflects the way data drift can be monitored and dealt with in the machine learning model at scale during production. As a part of the pipeline, implementing a system that periodically trains the model after some time t, or once it detects a drift using some of the methods aforementioned is the most robust way to overcome drift at a production level. Figure 3. ML model solution flow at production with data drift being monitored  

6.   Conclusions and Future Scope

This paper brings to forefront the concept of data drift an often missed dimension in setting up a ML workflow in production systems.  The concept of data drift is demonstrated with simulated data  in this study, where we further discuss in detail  some key metrics to measure the same. A high level ML pipeline is also mentioned on incorporating the drift related error correction in any ML workflow. With this work we aim to highlight that drift modelling and correction is not to be taken lightly and should be a part and parcel of any automated ML pipeline in production systems.  

7.   References

Webb, G. I., Lee, L. K., Goethals, B., & Petitjean, F. (2024). Analyzing concept drift and shift from sample data. Data Mining and Knowledge Discovery, 32(5), 1179-1199.

Bifet, Albert, and Ricard Gavalda. “Learning from time-changing data with adaptive windowing.” In Proceedings of the 2007 SIAM international conference on data mining, pp. 443-448. Society for Industrial and Applied Mathematics, 2007.

João Gama, Pedro Medas, Gladys Castillo, Pedro Pereira Rodrigues: Learning with Drift Detection. SBIA 2004: 286-295

Early Drift Detection Method. Manuel Baena-Garcia, Jose Del Campo-Avila, Raúl Fidalgo, Albert Bifet, Ricard Gavalda, Rafael Morales-Bueno. In Fourth International Workshop on Knowledge Discovery from Data Streams, 2006.

Frías-Blanco I, del Campo-Ávila J, Ramos-Jimenez G, et al. Online and non-parametric drift detection methods based on Hoeffding’s bounds. IEEE Transactions on Knowledge and Data Engineering, 2014, 27(3): 810-823.

Christoph Raab, Moritz Heusinger, Frank-Michael Schleif, Reactive Soft Prototype Computing for Concept Drift Streams, Neurocomputing, 2023

S. Page. 1954. Continuous Inspection Schemes. Biometrika 41, 1/2 (1954), 100–115.

Authors:

Dr.  Anish Roy Chowdhury is currently an Industry Data Science Leader at    a Leading Digital Services Organization.  In previous roles he was with ABInBev as a Data Science Research lead working in areas of Assortment Optimization, Reinforcement Learning to name a few, He also led several machine learning projects in areas of Credit Risk, Logistics and Sales forecasting. In his stint with HP Supply Chain Analytics he developed data Quality solutions for logistics projects and worked on building statistical models to predict spares part demands for large format printers. Prior to HP, he has 6 years of Work Experience in the IT sector as a DataBase Programmer.  During his stint in IT he has worked for Credit Card Fraud Detection among other Analytics related Projects.   He has a PhD in Mechanical Engineering (IISc Bangalore) . He also holds a MS degree in Mechanical Engineering from Louisiana State Univ. USA. He did his undergraduate studies from NIT Durgapur with published research in GA- Fuzzy Logic applications to Medical diagnostics Dr. Anish is also a highly acclaimed public speaker with numerous best presentation awards from National and international conferences and has also conducted several workshops in Academic institutes on R programming and MATLAB. He also has several academic publications to his credit and is also a Chapter Co-Author for a Springer Publication and a Oxford University Press, best selling publication in MATLAB.   Paulami Das is a seasoned Analytics Leader with 14 years’ experience across industries. She is passionate about helping businesses tackle complex problems through Machine Learning. Over her career, Paulami has worked several large and complex Machine Learning-centric projects around the globe.

Python Polynomial Regression In Machine Learning

Introduction

The link between the dependent and independent variables, Y and X, is modelled as the nth degree of the polynomial in polynomial regression, a type of linear regression. In order to draw the best line using data points, this is done. Let’s explore more about the Polynomial regression in this article.

Polynomial Regression

One of the rare instances of multiple linear regression models is polynomial regression. In other words, it is a sort of linear regression when the dependent and independent variables have a curvilinear connection to one another. In the data, a polynomial connection is fitted.

Additionally, by incorporating several polynomial parts, a number of linear regression equations are transformed into polynomial regression equations.

Need of Polynomial Regression

A few criteria that specify the requirement for polynomial regression are listed below.

If a linear model is used to a linear database, as is the case with simple linear regression, a good result is produced. However, a significant output is calculated if this model is applied to a non-linear dataset with no adjustments. These result in increased mistake rates, a drop in accuracy, and an increase inside the loss function.

Polynomial regression is required in situations when the data points are organized non-linearly.

A linear model won’t cover any data points if a non-linear model is available and we attempt to cover it. In order to guarantee that all of the data points are covered, a polynomial model is employed. Nevertheless, a curve rather than a straight line will work well for most data points when employing polynomial models.

A scatter diagram of residuals (Y-axis) here on predictor (X-axis) will show regions of many positive residuals inside the middle if we attempt to fit a linear model to curved data. As a result, it is inappropriate in this circumstance.

Polynomial Regression Applications

Basically, these are employed to define or enumerate non-linear phenomena.

The rate of tissue growth.

Progression of pandemic disease.

Carbon isotope distribution in lake sediments.

Modeling the estimated return of a dependent variable y in relation to the value of an independent variable x is the fundamental aim of regression analysis. We used the equation below in simple regression

y = a + bx + e

Here, the dependent variable is y, along with the independent variables a, b, and e.

Polynomial Regression Types

Numerous varieties of polynomial regression exist since a polynomial equation’s degree has no upper bound and can go up to the nth number. For instance, the second degree of a polynomial equation is typically expressed as a quadratic equation when spoken. As indicated, this degree is valid up to the nth number, and we are free to deduce quite so many equations as we require or want. As a result, polynomial regression is typically categorized as follows.

When the degree is 1, linear.

Equation has a quadratic degree of two.

Depending on the degree used, cubic with a degree as three continues.

When examining the output of chemical synthesis in terms of the temperature where the synthesis takes place, for instance, this linear model will frequently not work out. In such circumstances, we employ a quadratic model.

y = a+b1x+b2+b2+e

Here, the error rate is e, the y-intercept is a, and y is the dependent variable on x.

Python Implementation of Polynomial Regression

Step 1 − Import datasets and libraries

Import the necessary libraries as well as the dataset for the polynomial regression analysis.

# Importing up the libraries import numpy as nm import matplotlib.pyplot as mplt import pandas as ps # Importing up the dataset data = ps.read_csv('data.csv') data

Output sno Temperature Pressure 0 1 0 0.0002 1 2 20 0.0012 2 3 40 0.0060 3 4 60 0.0300 4 5 80 0.0900 5 6 100 0.2700

Step 2 − The dataset is split into two components in step two.

Divide the dataset into the X and y components. X will contain the columns 1 and 2. The two columns will be in column y.

X = data.iloc[:, 1:2].values y = data.iloc[:, 2].values

Step 3 − Dataset fitting with linear regression

Two components of the linear regression model are fitted.

from sklearn.linear_model import LinearRegressiondata line2 = LinearRegressiondata() line2.fit(X, y)

Step 4 − Polynomial Regression Fitting to the Dataset

X and Y are the two components to which the polynomial regression model is fit.

from sklearn.preprocessing import PolynomialFeaturesdata poly = PolynomialFeaturesdata(degree = 4) X_polyn = polyn.fit_transform(X) polyn.fit(X_polyn, y) line3 = LinearRegressiondata() line3.fit(X_polyn, y)

Step 5 − In this stage, we are utilizing a scatter plot to visualize the results of the linear regression.

mplt.scatter(X, y, color = 'blue') mplt.plot(X, lin.predict(X), color = 'red') mplt.title('Linear Regression') mplt.xlabel('Temperature') mplt.ylabel('Pressure') mplt.show()

Output

Step 6 − Using a scatter plot to display the polynomial regression findings.

mplt.scatter(X, y, color = 'blue') mplt.plot(X, lin2.predict(polyn.fit_transform(X)), color = 'red') mplt.title('Polynomial Regression') mplt.xlabel('Temperature') mplt.ylabel('Pressure') mplt.show()

Output

Step 7 − Use both linear & polynomial regression to forecast future outcomes. A NumPy 2D array must contain the input variable, it should be noted.

Linear Regression

predic = 110.0 predicdarray = nm.array([[predic]]) line2.predict(predicdarray)

Output Array([0.20657625]) Polynomial Regression

Predic2 = 110.0 predic2array = nm.array([[predic2]]) line3.predicdict(polyn.fit_transform(predicd2array))

Output Array([0.43298445]) Advantages

It is capable of doing a wide variety of tasks.

In general, polynomial suits a large range of curved surfaces.

The closest representation of the relationship between variables is provided by polynomials.

These are extremely responsive to deviations.

The outcomes of a nonlinear analysis might be significantly impacted by the existence of one or two variables.

Additionally, compared to linear regression, there are unfortunately less model validation techniques available for the discovery of deviations in nonlinear regression.

Conclusion

We have learned the theory underlying polynomial regression in this article. We learned the implementation of the Polynomial regression.

After applying this model to a real dataset, we could see its graph and utilize it to predict things. We hope this session was beneficial and that we can now confidently apply this knowledge to other datasets.

What Is Epoch In Machine Learning?

Introduction

The learning component of artificial intelligence (AI) is indeed the focus of the area of machine learning. Algorithms that represent a set of data are used to create this learning component. To train machine learning models, certain datasets are sent through the algorithm.

This article will define the term “Epoch,” which is used in machine learning, as well as other related topics like iterations, stochastic gradient descent. Anyone studying deep learning and machine learning or attempting to pursue a career in this industry must be familiar with these terms.

Epoch in ML

In machine learning, an epoch is a complete iteration through a dataset during the training process of a model. It is used to measure the progress of a model’s learning, as the number of epochs increases, the model’s accuracy and performance generally improves.

During the training process, a model is presented with a set of input data, called the training dataset, and the model’s goal is to learn a set of weights and biases that will allow it to accurately predict the output for unseen data. The training process is done by adjusting the model’s weights and biases based on the error it makes on the training dataset.

An epoch is a single pass through the entire training dataset, in which all the examples are used to adjust the model’s weights and biases. After one epoch, the model’s weights and biases will be updated, and the model will be able to make better predictions on the training data. The process is repeated multiple times, with the number of repetitions being referred to as the number of epochs.

The number of epochs is a hyper parameter, which means that it is a value that is set by the user and not learned by the model. The number of epochs can have a significant impact on the model’s performance. If the number of epochs is too low, the model will not have enough time to learn the patterns in the data, and its performance will be poor. On the other hand, if the number of epochs is too high, the model may over-fit the data, meaning that it will perform well on the training data but poorly on unseen data.

Determination of Epoch

One way to determine the optimal number of epochs is to use a technique called early stopping. This involves monitoring the model’s performance on a validation dataset, which is a set of data that the model has not seen before. If the model’s performance on the validation dataset stops improving after a certain number of epochs, the training process is stopped, and the model’s weights and biases are saved. This prevents the model from overfitting the training data.

Another way to determine the optimal number of epochs is to use a technique called learning rate scheduling. This involves decreasing the learning rate, which is the rate at which the model’s weights and biases are updated, as the number of epochs increases. A high learning rate can cause the model to overshoot the optimal solution, while a low learning rate can cause the model to converge too slowly.

In general, the number of epochs required to train a model will depend on the complexity of the data and the model. Simple models trained on small datasets may require only a few epochs, while more complex models trained on large datasets may require hundreds or even thousands of epochs.

Example of Epoch

Let’s use an illustration to clarify Epoch. Think about a dataset with 200 samples. These samples require the dataset to go through the model 1000 times, or 1000 epochs. The batch size is five. This indicates that the model weights are modified after each of the 40 batches, each of which contains five samples. Consequently, 40 updates will be made to the model.

Stochastic Gradient Descent

Stochastic gradient descent, or SGD, is an algorithm for optimization. It is employed in deep learning neural networks to train machine learning algorithms. This optimizing algorithm’s job is to find a set of internal model parameters that perform better than other performance indicators like mean squared error or logarithmic loss.

The process of optimization can be compared to a learning-based search. Gradient descent is the name of the optimization algorithm used here. The terms “gradient” and “descent” refer to movement down a slope in the direction of a desired minimal error level, respectively. The terms “gradient” describes the calculation of an error gradient or slope of error.

The search process can be repeated over distinct steps thanks to the algorithm. The goal of doing this is to marginally enhance the model parameters with each phase. The algorithm is iterative because of this property.

Predictions are made at each stage utilising samples and the existing internal parameters. Then, the forecasts are contrasted with the actual anticipated results. The internal model parameters are then modified after calculating the error. Different algorithms employ various update techniques. The backpropagation method is what the algorithm employs when it comes to artificial neural networks.

Iteration

An iteration is the total number of batches necessary to finish one epoch. The total convergence rate for one Epoch is equal to the number of batches.

Here is an illustration that can help explain what an iteration is.

Let’s say that training a machine learning model requires 5000 training instances. It is possible to divide this enormous data set into smaller units known as batches.

If the batch size is 500, ten batches will be produced. One Epoch would require ten iterations to finish.

Conclusion

In conclusion, an epoch is a single pass through the entire training dataset during the training process of a model. It is used to measure the progress of a model’s learning and the number of epochs can have a significant impact on the model’s performance. Determining the optimal number of epochs requires techniques such as early stopping and learning rate scheduling. The number of epochs required to train a model will depend on the complexity of the data and the model.

12 Powerful Tips To Ace Data Science And Machine Learning Hackathons

Overview

Data science hackathons can be a tough nut to crack, especially for beginners

Here are 12 powerful tips to crack your next data science hackathon!

Introduction

Like any discipline, data science also has a lot of “folk wisdom”. This folk wisdom is hard to teach formally or in a structured manner but it’s still crucial for success, both in the industry as well as in data science hackathons.

Newcomers in data science often form the impression that knowing all machine learning algorithms would be a panacea to all machine learning problems. They tend to believe that once they know the most common algorithms (Gradient Boosting, Xtreme Gradient Boosting, Deep Learning architectures), they would be able to perform well in their roles/organizations or top these leaderboards in competitions.

Sadly, that does not happen!

If you’re reading this, there’s a high chance you’ve participated in a data science hackathon (or several of them). I’ve personally struggled to improve my model’s performance in my initial hackathon days and it was quite a frustrating experience. I know a lot of newcomers who’ve faced the same obstacle.

So I decided to put together 12 powerful hacks that have helped me climb to the top echelons of hackathon leaderboards. Some of these hacks are straightforward and a few you’ll need to practice to master.

If you are a beginner in the world of Data Science Hackathons or someone who wants to master the art of competing in hackathons, you should definitely check out the third edition of HackLive – a guided community hackathon led by top hackers at Analytics Vidhya.

The 12 Tips to Ace Data Science Hackathons

Understand the Problem Statement

Build your Hypothesis Set

Team Up

Create a Generic Codebase

Feature Engineering is the Key

Ensemble (Almost) Always Wins

Discuss! Collaborate!

Trust Local Validation

Keep Evolving

Build hindsight to improve your foresight

Refactor your code

Improve iteratively

Data Science Hackathon Tip #1: Understand the Problem Statement

Seems too simple to be true? And yet, understanding the problem statement is the very first step to acing any data science hackathon:

Without understanding the problem statement, the data, and the evaluation metric, most of your work is fruitless. Spend time reading as much as possible about them and gain some functional domain knowledge if possible

Re-read all the available information. It will help you in figuring out an approach/direction before writing a single line of code. Only once you are very clear about the objective, you can proceed with the data exploration stage

Let me show you an example of a problem statement from a data science hackathon we conducted. Here’s the Problem Statement of the BigMart Sales Prediction problem:

The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and find out the sales of each product at a particular store.

Using this model, BigMart will try to understand the properties of products and stores which play a key role in increasing sales.

The idea is to find the properties of a product and store which impact the sales of a product. Here, you can think of some of the factors based on your understanding that can make an impact on the sales and come up with some hypotheses without looking at the data.

Data Science Hackathon Tip #2: Build your Hypothesis Set

Next, you should build a comprehensive list of hypotheses. Please note that I am actually asking you to build a set of the hypothesis before looking at the data. This ensures you are not biased by what you see in the data

It also gives you time to plan your workflow better. If you are able to think of hundreds of features, you can prioritize which ones you would create first

Read more about hypothesis generation here

I encourage you to go through the hypotheses generation stage for the BigMart Sales problem in this article: Approach and Solution to break in Top 20 of Big Mart Sales prediction We have divided them on the basis of store level and product level. Let me illustrate a few examples here.

Store-Level Hypotheses:

City type: Stores located in urban or Tier 1 cities should have higher sales because of the higher income levels of people there

Population Density: Stores located in densely populated areas should have higher sales because of more demand

Store Capacity: Stores that are very big in size should have higher sales as they act like one-stop-shops and people would prefer getting everything from one place

Ambiance: Stores that are well-maintained and managed by polite and humble people are expected to have higher footfall and thus higher sales

Product-Level Hypotheses:

Brand: Branded products should have higher sales because of higher trust in the customer

Packaging: Products with good packaging can attract customers and sell more

Utility: Daily products should have a higher tendency to sell as compared to the specific products

Promotional Offers: Products accompanied by attractive offers and discounts will sell more

Data Science Hackathon Hack #3: Team Up!

Build a team and brainstorm together. Try and find a person with a complementary skillset in your team. If you have been a coder all your life, go and team up with a person who has been on the business side of things

This would help you get a more diverse set of hypotheses and would increase your chances of winning the hackathon. The only exception to this rule can be that both of you should prefer the same tool/language stack

It will save you a lot of time and you will be able to parallelly experiment with several ideas and climb to the top of the leaderboard

Get a good score early in the competition which helps in teaming up with higher-ranked people

Here are some of the instances where hackathons were won by a team:

Data Science Hackathon Tip #4: Create a Generic Codebase

Save valuable time when you participate in your next hackathon by creating a reusable generic code base & functions for your favorite models which can be used in all your hackathons, like:

Create a variety of time-based features if the dataset has a time feature

You can write a function that will return different types of encoding schemes

You can write functions that will return your results on a variety of different models so that you can choose your baseline model wisely and choose your strategy accordingly

Here is a code snippet that I generally use to encode all my train, test, and validation set of the data. I just need to pass a dictionary on which column and what kind of encoding scheme I want. I will not recommend you to use exactly the same code but will suggest you keep some of the function handy so that you can spend more time on brainstorming and experimenting.

View the code on Gist.

Here is a sample of how I use the above function. I just need to provide a dictionary where the keys are the type of encoding I want and the values are the columns name that I want to encode:

View the code on Gist.

You can also use libraries like pandas profiling to get an idea about the dataset by reading the data:



Data Science Hackathon Tip #5: Feature Engineering is Key

“More data beats clever algorithms, but better data beats more data.”

– Peter Norwig

Feature engineering! This is one of my favorite parts of a data science hackathon. I get to tap into my creative juices when it comes to feature engineering – and which data scientist doesn’t like that?

Data Science Hackathon Tip #6: Ensemble (Almost) Always Wins

95% of winners have used ensemble models in their final submission on DataHack hackathons

Ensemble modeling is a powerful way to improve the performance of your model. It is an art of combining diverse results of individual models together to improvise on the stability and predictive power of the model

You will not find any data science hackathon that has top finishing solutions without ensemble models

You can learn more about the different ensemble techniques from the following articles:

Basics of Ensemble Learning

A Comprehensive Guide to Ensemble Learning

Data Science Hackathon Tip #7: Discuss! Collaborate!

Stay up to date with forum discussions to make sure that you are not missing out on any obvious detail regarding the problem

Do not hesitate to ask people in forums/messages:

Data Science Hackathon Tip #8: Trust Local Validation

Do not jump into building models by dumping data into the algorithms. While it is useful to get a sense of basic benchmarks, you need to take a step back and build a robust validation framework

Without validation, you are just shooting in the dark. You will be at the mercy of overfitting, leakage and other possible evaluation issues

By replicating the evaluation mechanism, you can make faster and better improvements by measuring your validation results along with making sure your model is robust enough to perform well on various subsets of the train/test data

Have a robust local validation set and avoid relying too much on the public leaderboard as this might lead to overfitting and can drop your private rank by a lot

In the Restaurant Revenue Prediction contest, a team that was ranked first on the public leaderboard slipped down to rank 1962 on the private leaderboard

“The first we used to determine which rows are part of the public leaderboard score, while the second is used to determine the correct predictions. Along the way, we encountered much interesting mathematics, computer science, and statistics challenges.”

Source: Kaggle:  BAYZ Team

Data Science Hackathon Tip #9 – Keep Evolving

It is not the strongest or the most intelligent who will survive but those who can best manage change. –Charles Darwin

If you are planning to enter the elite class of data science hackers then one thing is clear – you can’t win with traditional techniques and knowledge.

Employing logistic regression or KNN in Hackathons can be a great starting point but as you move ahead of the curve, these won’t land you in the top 100.

Let’s take a simple example – in the early days of NLP hackathons, participants used TF-IDF, then Word2vec came around. Fast-forward to nowadays, there are state-of-the-art Transformers. The same goes for computer vision and so on.

Keep yourself up-to-date with the latest research papers and projects on Github. Although this will require a bit of extra effort, it will be worth it.

Data Science Hackathon Tip #10 – Use Hindsight to build your Foresight

Has it ever happened that after the competition is over, you sit back, relax, maybe think about the things you could have done, and then move on to the next competition? Well, this is the best time to learn!

Do not stop learning after the competition is over. Read winning solutions of the competition, analyze where you went wrong. After all, learning from mistakes can be very impactful!

Try to improve your solutions. Make notes about it. Refer to it to your friends and colleagues and take back feedback.

This will give you a solid head-start for your next competition. And this time you’ll be much more equipped to go tackle the problem statement. Datahack provides a really cool feature of late submission. You can make changes to your code even after the hackathon is over and submit the solution and check its score too!

Data Science Hackathon Tip #11 – Refactor your code

Just imagine living in a room, where everything is messy, clothes lying all around, shoes on the shelves, and food on the floor. It is nasty. Isn’t it? The same goes for your code.

When we get started with a competition, we are excited and we probably write rough code, copy-paste from earlier your earlier notebooks, and some from stack overflow. Continuing this trend for the complete notebook will make it messy. Understanding your code will consume the majority of your time and make it harder to perform operations.

The solution is to keep refactoring your code from time to time. Keep maintaining your code at regular intervals of time.

This will also help you team up with other participants and have much better communication.

Data Science Hackathon Tip #12 – Improve iteratively

Many of us follow the linear approach of model building, going through the same process – Data Cleaning, EDA, feature engineering, model building, evaluation. The trick is to understand that it is a circular and iterative process.

For example, we are building a sales prediction model and we get a low MAE, we decide to analyze the samples. Hence, it turns out, that our model is giving spurious results for female buyers. We can then take a step back and focus on the Gender feature, do EDA on it and check how to improve from there. Here we are going back and forth and improving step-by-step.

We can also look at some of the strong and important features and combine some of them and check their results. We may or may not see an improvement but we can only figure it out by moving iteratively.

It is very important to understand that the trick is to be a dynamic learner. Following an iterative process can lead you to achieve double or single-digit ranks.

Final Thoughts

These 12 hacks have held me in good stead regardless of the hackathon I’m participating in. Sure, a few tweaks here and there are necessary but having a solid framework and structure in place will take you a long way towards achieving success in data science hackathons.

Do you want more of such hacks, tips, and tricks? HackLive is the way to go from zero-to-hero and master the art of participating in a data science competition. Don’t forget to check out the third edition of HackLive 3.

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