Regularization is a technique used to prevent overfitting in machine learning models by adding a penalty to the loss function, which discourages the model from becoming too complex. […]

k-Nearest Neighbors (k-NN) is one of the simplest and most intuitive machine learning algorithms used primarily for classification, though it can also be applied to regression tasks. It’s […]

Random Forest is an ensemble learning method that builds multiple decision trees and merges them together to get a more accurate and stable prediction. It’s one of the […]

A decision tree is a supervised learning algorithm used for both classification and regression tasks. It models decisions and their possible consequences in a tree-like structure, where each […]

Logistic regression is a widely used statistical method for binary classification problems, where the outcome or target variable is categorical and typically has two possible values (e.g., 0/1, […]

Linear regression is a fundamental statistical method used to predict a continuous outcome (dependent variable) based on one or more input features (independent variables). The goal is to […]

Supervised machine learning is a foundational area in data science and machine learning. Here are some important topics within supervised machine learning: 1. Types of Supervised Learning Algorithms […]

The mean, median, and mode are measures of central tendency, each providing different insights into the distribution of a dataset. Here’s how they differ: 1. Mean Definition: The […]

Here’s a list of important interview questions in statistics that are commonly asked in the context of Machine Learning: 1. Descriptive Statistics 2. Probability 3. Probability Distributions 4. […]