Ridge Regression

In layman’s terms, Ridge regression adds one more term to linear regression’s cost function to reduce error. Ridge regression is a model-tuning method used to analyze data that suffer from multicollinearity. This method performs L2 regularization. When the issue of multicollinearity occurs, least-squares are unbiased, and variances are large; this results in predicted values being…

Decision (Regression) tree

The decision tree is one of the most commonly used algorithms in supervised learning. You can use it for both regression and classification when we use this algorithm for regression problems, it is called a regression tree. In real life, decision trees are very famous algorithms they are used to predict high occupancy dates for…

Polynomial regression

“Polynomial Regression is a form of linear regression in which the relationship between the independent variable x and dependent variable y is modeled as an nth-degree polynomial. Polynomial regression fits a nonlinear relationship between the value of x and the corresponding conditional mean of y.” As stated in formal terms, this definition of polynomial regression…

Linear Regression

There are situations in daily life where we want to know the relationship between various factors, for example, if the price of petrol increases would it affect the sales of cars or does changing the location of the house will it affect the price. The process of finding this relationship between multitudes of factors is…

K-Nearest Neighbour

K Nearest neighbour (KNN) is one of the most basic classification algorithms. It follows the assumption that similar things exist in close proximity to each other. The K-nearest neighbour algorithm calculates the distance between the various points whose category is known and then selects the shortest distance for the new data point. It is an…

Logistic Regression from Scratch

Lets us try to implement logistic regression from scratch in python. Recommended to be read after the Neural Networks release. Importing necessary libraries The dataset we will be using is Pima-Indians-diabetes-database Whose objective is to predict whether or not a patient has diabetes diagnostically. y = data.Outcome.values x_data = data.drop([“Outcome”],axis=1)   After this, the data…

Logistic Regression

Logistic regression is one of the most popular algorithms for classification problems. It is called regression even though it is not a regression algorithm because the underlying technology is similar to Linear Regression. The term “logistic” comes from the statistical model used (logit model). As seen in earlier releases, classification algorithms are used to classify…