Ganga Sweets Interview Questions and Answers

Bagging and boosting are ensemble learning techniques. Tree based models are decision trees used for classification and regression.

• Bagging (Bootstrap Aggregating) involves training multiple models on different subsets of the training data and combining their predictions.

• Boosting involves training multiple models sequentially, with each model correcting the errors of its predecessor.

• Different types of learning models include decision trees, random forests, gradient boosting ma...read more

Logistic regression is used for binary classification while linear regression is used for regression tasks.

• Logistic regression predicts the probability of a binary outcome (0 or 1) based on input features.

• Linear regression predicts a continuous value based on input features.

• Logistic regression uses a sigmoid function to map predicted values between 0 and 1.

• Linear regression uses a linear equation to model the relationship between input and output variables.

• Example: Predicting...read more

KNN is a supervised machine learning algorithm used for classification and regression. K-means is an unsupervised clustering algorithm.

• KNN stands for K-Nearest Neighbors and works by finding the K closest data points to a given data point to make predictions.

• K-means is a clustering algorithm that partitions data into K clusters based on similarity.

• KNN is used for classification tasks, while K-means is used for clustering tasks.

• Example: KNN can be used to predict whether a cus...read more

Supervised learning is a type of machine learning where the model is trained on labeled data.

• In supervised learning, the algorithm learns from labeled training data to make predictions or decisions.

• It involves mapping input data to the correct output label based on the input-output pairs provided during training.

• Common examples include classification and regression tasks, such as predicting whether an email is spam or determining house prices.

• The goal is for the model to gene...read more

Unsupervised learning is a type of machine learning where the model learns patterns from unlabeled data.

• No explicit labels are provided in unsupervised learning

• The model must find patterns and relationships in the data on its own

• Clustering and dimensionality reduction are common techniques in unsupervised learning

Random forest is an ensemble learning method that builds multiple decision trees and merges them to improve accuracy and prevent overfitting.

• Random forest is a collection of decision trees that are trained on random subsets of the data.

• Each tree in the random forest independently predicts the target variable, and the final prediction is made by averaging the predictions of all trees.

• Random forest is effective in handling high-dimensional data and can handle missing values and...read more