Classification Video Lecture | Reasoning Aptitude for Competitive Examinations - Bank Exams

Ans. Classification plays a crucial role in machine learning as it is a supervised learning technique used to categorize data into predefined classes or labels. It helps in identifying patterns, making predictions, and gaining insights from the data by assigning labels to new, unseen data based on the patterns learned from the training data.

Ans. While classification aims to predict the class or category of a given input, regression focuses on predicting a continuous numerical value. Classification deals with discrete outputs, whereas regression deals with continuous outputs. In classification, the output is a class label, while in regression, it is a real value.

Ans. There are various algorithms used for classification in machine learning, including: - Decision Trees: These models use a tree-like structure to make decisions based on features of the data. - Logistic Regression: It predicts the probability of an instance belonging to a particular class using a logistic function. - Support Vector Machines (SVM): SVM finds the best hyperplane that separates data into different classes. - Random Forest: It combines multiple decision trees to make predictions by averaging their outputs. - Naive Bayes: This algorithm applies Bayes' theorem with the assumption of independence between features to classify data.

Ans. The performance of a classification model can be evaluated using various metrics, including: - Accuracy: Measures the proportion of correctly classified instances. - Precision: Represents the ratio of true positive instances to the total predicted positive instances. - Recall: Measures the ratio of true positive instances to the total actual positive instances. - F1 Score: A balanced measure that combines precision and recall. - Confusion Matrix: A table that shows the counts of true positives, true negatives, false positives, and false negatives.

Ans. Yes, classification models can handle imbalanced datasets, but it requires careful consideration. Some techniques to address imbalanced datasets include: - Resampling: Over-sampling the minority class or under-sampling the majority class to balance the dataset. - Changing the Performance Metric: Instead of accuracy, metrics like precision, recall, or F1 score can be used to evaluate model performance on imbalanced datasets. - Using Ensemble Methods: Techniques like boosting and bagging can improve the performance of classification models on imbalanced datasets by combining multiple models.