Matplotlib

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

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…

By KGP Talkie, ago

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…

By KGP Talkie, ago

Logistic Regression with Python in Machine Learning | KGP Talkie

What is Logistic Regression? Logistic Regression is a Machine Learning algorithm which is used for the classification problems, it is a predictive analysis algorithm and based on the concept of probability. Logistic regression is basically a supervised classification algorithm. In a classification problem, the target variable(or output),y, can take only 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