Feature Engineering Tutorial Series 4: Linear Model Assumptions

Linear models make the following assumptions over the independent variables X, used to predict Y: There is a linear relationship between X and the outcome Y The independent variables X are normally distributed There is no or little co-linearity among the independent variables Homoscedasticity (homogeneity of variance) Examples of linear Read more…

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…

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…

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…

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…

Recursive Feature Elimination (RFE) by Using Tree Based and Gradient Based Estimators | Machine Learning | KGP Talkie

Recursive Feature Elimination (RFE) Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH As it’s name suggests, it eliminates the features recursively and build a model using remaining attributes then again calculates the model accuracy of the model..Moreover how it do it train the model on all the dataset and it tries to remove the least performing Read more…

Step Forward, Step Backward and Exhaustive Feature Selection | Wrapper Method | KGP Talkie

Wrapping method Uses of Wrapping method Use combinations of variables to determine predictive power. To find the best combination of variables. Computationally expensive than filter method. To perform better than filter method. Not recommended on high number of features. Forward Step Selection In this wrapping method, it selects one best Read more…

Lasso and Ridge Regularisation for Feature Selection in Classification | Embedded Method | KGP Talkie

What is Regularisation? Regularization adds a penalty on the different parameters of the model to reduce the freedom of the model. Hence, the model will be less likely to fit the noise of the training data and will improve the generalization abilities of the model. There are basically 3-types of Read more…