“Mastering Naïve Bayes: Advancements and Techniques” Before moving forward pls read this article first then you can understand further …. "Demystifying Naïve Bayes: Simple yet Powerful for Text Classification" "Harnessing Naïve Bayes Algorithm for Effective Text Analysis and Classification"vocal.media Agenda: Advancing Naïve Bayes Types of Naïve Bayes The Concept of Log Probability The Concept of Laplace Smoothing

Linear regression relies on several assumptions to ensure the validity and reliability of the estimates and inferences. Google Colaboratory Edit descriptioncolab.research.google.com The key assumptions of linear regression are: Linearity Normality of Residuals Homoscedasticity No Autocorrelation No or little Multicollinearity. The Assumption # 1. Linearity There is a linear relationship between the independent variables and the dependent variable. …

“Harnessing Naïve Bayes Algorithm for Effective Text Analysis and Classification” Naïve Bayes Naïve Bayes is, first of all, a classification algorithm that helps us solve classification-related problems. Not only does it assist in classification, but it particularly excels in analyzing textual data. If you are working with text or need to perform…

Unraveling the Power of K-Nearest Neighbors: A Comprehensive Guide to the KNN Algorithm K-Nearest Neighbors (KNN) K-Nearest Neighbors (KNN) is a simple, yet powerful machine learning algorithm used for both classification and regression tasks. It makes predictions based on the similarity of input data to its neighboring data points. Intuition: The intuition behind…

Topics that we are going to discuss Bias variance tradeoff is. what is Bias variance overfitting underfitting bias variance tradeoff bias variance decomposition The Hidden Truth To understand the bias-variance tradeoff, let’s start with the example you provided. You have a dataset consisting of the CGPA, IQ, and LPA (salary) of 1000 college students. …

Regularization is a technique used in machine learning to prevent overfitting, which occurs when a model becomes too complex and performs well on the training data but poorly on new, unseen data. …

“Unlocking the Power of ROC AUC Curve: A Practical Approach” Why ROC Curve? In supervised machine learning, we encounter two types of problems: regression and classification. In a regression problem, we aim to predict a numerical value, such as predicting the salary (LPA) based on features like CGPA and IQ. On the other…

“A Step-by-Step Journey Through Dimensionality Reduction and Data Exploration” Follow me on LinkedIn https://www.linkedin.com/in/ajay-mehta-069339258 practical implementation 100-days-of-machine-learning/day47-pca/pca_step_by_step (1).ipynb at main · campusx-official/100-days-of-machine-learning · GitHub https://www.kaggle.com/nitsin/pca-demo-1 In simple terms, PCA (Principal Component Analysis) is a technique used to simplify and understand complex data. …

Strategies for Reliable Predictions” WHY? Data scientists typically follow a process where they start by taking a dataset and perform a train-test-split. The purpose of this split is to divide the data into training and testing sets. The training data is used to train the model, while the testing data is…