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...
Data Science Team
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...
Singular value decomposition
Principal component analysis and singular value decomposition are among the two common concepts of linear algebra in machine learning. After collecting raw data, is...
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...
