# Tag Archives: Data Science

## Validation Curves Explained – Python Sklearn Example

In this post, you will learn about validation curves with Python Sklearn example. You will learn about how validation curves can help diagnose or assess your machine learning models in relation to underfitting and overfitting. On the similar topic, I recommend you reading one of the previous post on assessing overfitting and underfitting titled Learning curves explained with Python Sklearn example. The following gets covered in this post: Why validation curves? Python Sklearn example for validation curves Why Validation Curves? As like learning curve, the validation curve also helps in diagnozing the model bias vs variance. The validation curve plot helps in selecting most appropriate model parameters (hyper-parameters). Unlike learning …

## Python – 5 Sets of Useful Numpy Unary Functions

In this post, you will learn about some of the 5 most popular or useful set of unary universal functions (ufuncs) provided by Python Numpy library. As data scientists, it will be useful to learn these unary functions by heart as it will help in performing arithmetic operations on sequential-like objects. These functions can also be termed as vectorized wrapper functions which are used to perform element-wise operations. The following represents different set of popular functions: Basic arithmetic operations Summary statistics Sorting Minimum / maximum Array equality Basic Arithmetic Operations The following are some of the unary functions whichc an be used to perform arithmetic operations: add, subtract, multiply, divide, …

## Learning Curves Explained with Python Sklearn Example

In this post, you will learn about how to use learning curves in learning curves using Python code (Sklearn) example to determine model bias-variance. Knowing how to use learning curves will help you assess/diagnose whether the model is suffering from high bias (underfitting) or high variance (overfitting) and whether increasing training data samples could help solve the bias or variance problem. Some of the following topics are covered in this post: Why learning curves? Python Sklearn example for the Learning curve You may want to check some of the following posts in order to get a better understanding of bias-variance and underfitting-overfitting. Bias-variance concepts and interview questions Overfitting/Underfitting concepts and interview …

## Logistic Regression Quiz Questions & Answers

In this post, you will learn about Logistic Regression terminologies / glossary with quiz / practice questions. For machine learning Engineers or data scientists wanting to test their understanding of Logistic regression or preparing for interviews, these concepts and related quiz questions and answers will come handy. Here is a related post, 30 Logistic regression interview practice questions I have posted earlier. Here are some of the questions and answers discussed in this post: What are different names / terms used in place of Logistic regression? Define Logistic regression in simple words? Define logistic regression in terms of logit? Define logistic function? What does training a logistic regression model mean? What are different types …

## K-Fold Cross Validation – Python Example

In this post, you will learn about K-fold Cross Validation concepts with Python code example. It is important to learn the concepts cross validation concepts in order to perform model tuning with an end goal to choose model which has the high generalization performance. As a data scientist / machine learning Engineer, you must have a good understanding of the cross validation concepts in general. The following topics get covered in this post: What and why of K-fold cross validation When to select what values of K? K-fold cross validation with python (using cross-validation generators) K-fold cross validation with python (using cross_val_score) What and Why of K-fold Cross Validation K-fold cross validation …

## Sklearn Machine Learning Pipeline – Python Example

In this post, you will learning about concepts about machine learning (ML) pipeline and how to build ML pipeline using Python Sklearn Pipeline (sklearn.pipeline) package. Getting to know how to use Sklearn.pipeline effectively for training/testing machine learning models will help automate various different activities such as feature scaling, feature selection / extraction and training/testing the models. It is recommended for data scientists (Python) to get a good understanding of Sklearn.pipeline. The following are some of the topics covered in this post: Introduction to ML Pipeline Sklearn ML Pipeline Python code example Introduction to ML Pipeline Machine Learning (ML) pipeline, theoretically, represents different steps including data transformation and prediction through which data …

## Imputing Missing Data using Sklearn SimpleImputer

In this post, you will learn about how to use Python’s Sklearn SimpleImputer for imputing / replacing numerical & categorical missing data using different strategies. In one of the related article posted sometime back, the usage of fillna method of Pandas DataFrame is discussed. Here is the link, Replace missing values with mean, median and mode. Handling missing values is key part of data preprocessing and hence, it is of utmost importance for data scientists / machine learning Engineers to learn different techniques in relation imputing / replacing numerical or categorical missing values with appropriate value based on appropriate strategies. The following topics will be covered in this post: SimpleImputer explained with Python …

## When to use LabelEncoder – Python Example

In this post, you will learn about when to use LabelEncoder. As a data scientist, you must have a clear understanding on when to use LabelEncoder and when to use other encoders such as One-hot Encoder. Using appropriate type of encoders is key part of data preprocessing in machine learning model building lifecycle. Here are some of the scenarios when you could use LabelEncoder without having impact on model. Use LabelEncoder when there are only two possible values of a categorical features. For example, features having value such as yes or no. Or, maybe, gender feature when there are only two possible values including male or female. Use LabelEncoder for …

## Feature Extraction using PCA – Python Example

In this post, you will learn about how to use principal component analysis (PCA) for extracting important features (also termed as feature extraction technique) from a list of given features. As a machine learning / data scientist, it is very important to learn the PCA technique for feature extraction as it helps you visualize the data in the lights of importance of explained variance of data set. The following topics get covered in this post: What is principal component analysis? PCA algorithm for feature extraction PCA Python implementation step-by-step PCA Python Sklearn example What is Principal Component Analysis? Principal component analysis (PCA) is an unsupervised linear transformation technique which is primarily used …

## PCA Explained Variance Concepts with Python Example

In this post, you will learn about the concepts of explained variance which is one of the key concepts related to principal component analysis (PCA). The explained variance concepts will be illustrated with Python code examples. Some of the following topics will be covered: What is explained variance? Python code examples of explained variance What is Explained Variance? Explained variance refers to the variance explained by each of the principal components (eigenvectors). It can be represented as a function of ratio of related eigenvalue and sum of eigenvalues of all eigenvectors. Let’s say that there are N eigenvectors, then the explained variance for each eigenvector (principal component) can be expressed the …

## Eigenvalues & Eigenvectors with Python Examples

In this post, you will learn about how to calculate Eigenvalues and Eigenvectors using Python code examples. Before getting ahead and learning the code examples, you may want to check out this post on when & why to use Eigenvalues and Eigenvectors. As a machine learning Engineer / Data Scientist, you must get a good understanding of Eigenvalues / Eigenvectors concepts as it proves to be very useful in feature extraction techniques such as principal components analysis. Python Numpy package is used for illustration purpose. The following topics are covered in this post: Creating Eigenvectors / Eigenvalues using Numpy Linalg module Re-creating original transformation matrix from eigenvalues & eigenvectors Creating Eigenvectors / Eigenvalues using Numpy In …

## Why & When to use Eigenvalues & Eigenvectors?

In this post, you will learn about why and when you need to use Eigenvalues and Eigenvectors? As a data scientist / machine learning Engineer, one must need to have a good understanding of concepts related to Eigenvalues and Eigenvectors as these concepts are used in one of the most popular dimensionality reduction technique – Principal Component Analysis (PCA). In PCA, these concepts help in reducing the dimensionality of the data (curse of dimensionality) resulting in the simpler model which is computationally efficient and provides greater generalization accuracy. In this post, the following topics will be covered: Background – Why need Eigenvalues & Eigenvectors? What are Eigenvalues & Eigenvectors? When to …

## Standard Deviation of Population & Sample – Python

In this post, you will learn about the statistics concepts of standard deviation with the help of Python code example. The following topics are covered in this post: What is Standard deviation? Different techniques for calculating standard deviation Standard deviation of population vs sample What is Standard Deviation? The Standard Deviation (SD) of a data set is a measure of how spread out the data is. Take a look at the following example using two different samples of 4 numbers whose mean are same but the standard deviation (data spread) are different. Here is the code for calculating the mean of the above sample. One can either write Python code …

## Sklearn SelectFromModel for Feature Importance

In this post, you will learn about how to use Sklearn SelectFromModel class for reducing the training / test data set to the new dataset which consists of features having feature importance value greater than a specified threshold value. This method is very important when one is using Sklearn pipeline for creating different stages and Sklearn RandomForest implementation (such as RandomForestClassifier) for feature selection. You may refer to this post to check out how RandomForestClassifier can be used for feature importance. The SelectFromModel usage is illustrated using Python code example. SelectFromModel Python Code Example Here are the steps and related python code for using SelectFromModel. Determine the feature importance using …

## Feature Importance using Random Forest Classifier – Python

In this post, you will learn about how to use Sklearn Random Forest Classifier (RandomForestClassifier) for determining feature importance using Python code example. This will be useful in feature selection by finding most important features when solving classification machine learning problem. It is very important to understand feature importance and feature selection techniques for data scientists to use most appropriate features for training machine learning models. Recall that other feature selection techniques includes L-norm regularization techniques, greedy search algorithms techniques such as sequential backward / sequential forward selection etc. The following are some of the topics covered in this post: Why feature importance? Random Forest for feature importance Using Sklearn RandomForestClassifier for Feature Importance Why …

## Sequential Forward Selection – Python Example

In this post, you will learn about one of feature selection techniques namely sequential forward selection with Python code example. Refer to my earlier post on sequential backward selection technique for feature selection. Sequential forward selection algorithm is a part of sequential feature selection algorithms. Some of the following topics will be covered in this post: Introduction to sequential feature selection algorithms Sequential forward selection algorithm Python example using sequential forward selection Introduction to Sequential Feature Selection Sequential feature selection algorithms including sequential forward selection algorithm belongs to the family of greedy search algorithms which are used to reduce an initial d-dimensional feature space to a k-dimensional feature subspace where k < d. …