sklearn

Feature Selection Based on Univariate ROC_AUC for Classification and MSE for Regression | Machine Learning | KGP talkie

Feature Selection Based on Univariate ROC_AUC for Classification and MSE for Regression Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH What is ROC_AUC The Receiver Operator Characteristic (ROC) curve is well-known in evaluating classification performance. Owing to its superiority in dealing with imbalanced and cost-sensitive data, the ROC curve has been exploited as a Read more…

By KGP Talkie, ago

Feature Selection using Fisher Score and Chi2 (χ2) Test | Titanic Dataset | Machine Learning | KGP Talkie

Feature Selection using Fisher Score and Chi2 (χ2) Test Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH What is Fisher Score and Chi2 ( χ2) Test Fisher score is one of the most widely used supervised feature selection methods. However, it selects each feature independently according to their scores under the Fisher criterion, which Read more…

By KGP Talkie, ago

Feature Selection Based on Univariate (ANOVA) Test for Classification | Machine Learning | KGP Talkie

Feature Selection Based on Univariate (ANOVA) Test for Classification Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH What is Univariate (ANOVA) Test The elimination process aims to reduce the size of the input feature set and at the same time to retain the class discriminatory information for classification problems. An F-test is any statistical Read more…

By KGP Talkie, ago

Feature Selection Based on Mutual Information (Entropy) Gain for Classification and Regression | Machine Learning | KGP Talkie

Feature Selection Based on Mutual Information (Entropy) Gain Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH What is Mutual Information The elimination process aims to reduce the size of the input feature set and at the same time to retain the class discriminatory information for classification problems. Mutual information (MI) is a measure of Read more…

By KGP Talkie, ago

Feature Selection with Filtering Method | Constant, Quasi Constant and Duplicate Feature Removal

Filtering method Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH Unnecessary and redundant features not only slow down the training time of an algorithm, but they also affect the performance of the algorithm. There are several advantages of performing feature selection before training machine learning models: Models with less number of features have higher Read more…

By KGP Talkie, ago

Feature Dimention Reduction Using LDA and PCA with Python | Principal Component Analysis in Feature Selection | KGP Talkie

Feature Dimension Reduction Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH What is LDA (Linear Discriminant Analysis)? The idea behind LDA is simple. Mathematically speaking, we need to find a new feature space to project the data in order to maximize classes separability Linear Discriminant Analysis is a supervised algorithm as it takes the Read more…

By KGP Talkie, ago

Use of Linear and Logistic Regression Coefficients with Lasso (L1) and Ridge (L2) Regularization for Feature Selection in Machine Learning

Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH Linear Regression Let’s first understand what exactly linear regression is, it is a straight forward approach to predict the response y on the basis of different prediction variables such x and ε. . There is a linear relation between x and y. 𝑦𝑖 = 𝛽0 + Read more…

By KGP Talkie, ago

PCA with Python | Principal Component Analysis Machine Learning | KGP Talkie

Principal Component Analysis(PCA) According to Wikipedia, PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. Principal components These Read more…

By KGP Talkie, ago

K-Mean Clustering in Python | Machine Learning | KGP Talkie

What is K-Mean Clustering? Machine Learning can broadly be classified into three types: Supervised Learning Unsupervised Learning Semi-supervised Learning K-means algorithm identifies k number of centroids, and then allocates every data point to the nearest cluster, while keeping the centroids as small as possible. The ‘means’ in the K-means refers Read more…

By KGP Talkie, ago

Random Forest Classifier and Regressor with python | Machine Learning | KGP Talkie

What is it? A Random Forest is an ensemble technique which can have capable of performing both regression and classification tasks with the use of multiple decision trees and a technique called Bootstrap and Aggregation, commonly known as bagging. The basic idea behind this is to combine multiple decision trees Read more…

By KGP Talkie, ago

SVM with Python | Support Vector Machines (SVM) Vector Machines Machine Learning | KGP Talkie

What is Support Vector Machines (SVM) We will start our discussion with little introduction about SVM. Support Vector Machine(SVM) is a supervised binary classification algorithm. Given a set of points of two types in N-dimensional place SVM generates a (N−1) dimensional hyperplane to separate those points into two groups. A Read more…

By KGP Talkie, ago