Classification

F1-Score The F1 Score is a metric used to evaluate the performance of a classification model by combining both precision and recall into a single score. It provides a balance between precision and recall, especially when there is an uneven class distribution or when both false positives and false negatives …

Precision Score Precision Score is a metric used in classification tasks to measure how many of the positive predictions made by a model are actually correct. In simpler terms, it answers the question: Out of all the instances the model predicted as positive, how many were truly positive? The formula …

Recall Score The Recall Score (also known as Sensitivity or True Positive Rate) measures the ability of a classification model to correctly identify all relevant (positive) instances. In other words, it answers the question: Out of all the actual positive cases, how many did the model correctly predict? The formula …

Accuracy, Precision, Recall, and F1-Score Accuracy, precision, recall, and F1-score are commonly used performance metrics to evaluate the effectiveness of a classification model. These metrics provide insights into different aspects of the model’s performance in predicting class labels. Here’s a brief explanation of each metric: 1. Accuracy: Accuracy measures the …

Support Vector What is SVC? The Support Vector Classifier (SVC) is a method used in machine learning to classify data into different groups. It helps to find the best line (or boundary) that separates different classes of data. How SVC Works¶ Hyperplane: Imagine a line in 2D or a flat …

K-NN K-Nearest Neighbors (K-NN) is a simple machine learning algorithm used for both classification and regression tasks. It’s based on the idea that things that are similar to each other are likely to be in the same category. Key Concepts of K-NN:¶ What is K-NN? K-NN is an algorithm that …

Naive Bayes Naive Bayes is a simple but powerful machine learning algorithm used for classification tasks. It’s based on applying Bayes’ Theorem with the assumption that features are independent of each other, which is why it’s called “naive.” Key Concepts of Naive Bayes:¶ What is Naive Bayes? Naive Bayes is …

Random Forest Random Forest Classifier in Simple Language¶What is a Random Forest Classifier? A Random Forest Classifier is a machine learning method used for classification tasks. It combines multiple decision trees to improve accuracy and robustness. Think of it as a team of decision trees that work together to make …

Decision Tree Decision Tree Classifier Explained Simply¶What is a Decision Tree Classifier? A Decision Tree Classifier is a method used in machine learning to categorize data into different groups. It works like a flowchart, where each question helps you narrow down the possible outcomes until you arrive at a final …

Logistic Regression Logistic Regression is a method used in machine learning to predict if something belongs to one of two categories, like “Yes” or “No.” Key Ideas:¶ What does it do? Logistic regression helps answer questions like, “Will a customer buy this product?” or “Is this email spam?” It takes …