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Multicollinearity

With increasingly advanced machine learning and deep learning algorithms, you can solve almost any problem with proper datasets. However, as the complexity of the...

Chi Test

When developing a machine learning model, you may encounter numerous problems. One common problem related to feature selection determines how relevant the input features...

Log Loss

In machine learning, you can solve predictive modeling through classification problems. For each observation in the model, you must predict the class label. The...

Relu Activation Function

Activate function is an essential element for designing a neural network. Choosing the activation function will give you complete control over the network model’s...

Confounding Variable

Confounding variable is a statistical term.The concept is a bit confusing for many people because of the method to use. For starters, different researchers...

Confusion Matrix

The classification process helps with the categorization of the dataset into different classes. A machine learning model enables you to: Frame the problem, Collect...

Feature Engineering

Every machine learning algorithm analyzes and processes input data and generates the outputs. The input data includes features in columns. These columns are structured...

Power Analysis

A hypothesis test for statistical power will help detect the probability of an effect. You can only spot the true effect if available. With...

SMOTE

This article will discuss how SMOTE module helps increase underrepresented numbers in the dataset of a machine learning model. SMOTE is the best method...