Exploring Machine Learning Concepts

What is the main goal of supervised learning?

• To classify data without any labels.

• To train a model to make predictions based on labeled data.

• To reduce the amount of data used for training.

• To create unsupervised models for clustering.

How does a decision tree make predictions?

• A decision tree makes predictions by using a single feature only.

• A decision tree predicts by randomly selecting a leaf node.

• A decision tree predicts by traversing from the root to a leaf node based on feature splits.

• A decision tree predicts by averaging all feature values.

What is the role of the kernel in Support Vector Machines?

• The kernel is responsible for selecting the best features from the input data.

• The kernel acts as a regularization parameter to prevent overfitting.

• The kernel reduces the dimensionality of the data for faster processing.

• The kernel transforms input data into a higher-dimensional space to enable better separation of classes.

Explain the assumption made by the Naive Bayes classifier.

• The features are dependent on each other.

• The features are correlated regardless of the class label.

• The class label is independent of the features.

• The features are conditionally independent given the class label.

What is the purpose of the sigmoid function in logistic regression?

• To increase the dimensionality of the data.

• To perform linear regression on the dataset.

• To calculate the mean of the predicted values.

• To map predicted values to probabilities for binary classification.

How does gradient descent optimize a function?

• Gradient descent optimizes a function by randomly selecting parameters.

• Gradient descent optimizes a function by iteratively updating parameters in the direction of the negative gradient.

• Gradient descent optimizes a function by using a fixed set of parameters.

• Gradient descent optimizes a function by maximizing the gradient.

What does KNN stand for and how does it work?

• K-Nearest Neighbors Method

• K-Nearest Neighbors

• K-Nearest Neighbors Algorithm

• K-Nearest Neighbors Classifier

Why is feature scaling important in machine learning?

• Feature scaling is only necessary for categorical data.

• Feature scaling is used to eliminate outliers from the data.

• Feature scaling ensures that all features contribute equally to the model, improving performance and convergence.

• Feature scaling reduces the number of features in the dataset.

What is the equation for a simple linear regression model?

• y = m + bx

• y = ax^2 + c

• y = mx + b

• y = mx^2 + b

How do Ridge and Lasso regression differ in terms of regularization?

• Ridge uses L1 regularization; Lasso uses L2 regularization.

• Both Ridge and Lasso use L2 regularization.

• Ridge and Lasso do not use any form of regularization.

• Ridge uses L2 regularization; Lasso uses L1 regularization.