Which of the following is the primary goal of "Feature Scaling" in machine learning?

• To normalize data

• To improve model accuracy

• To reduce overfitting

• To adjust features to a common scale so that the model doesn't give undue importance to one feature

Which technique is used for dimensionality reduction in Data Science?

• K-Nearest Neighbors (KNN)

• Principal Component Analysis (PCA)

• Naive Bayes

• Decision Trees

Which of these is a type of unsupervised learning?

• Linear Regression

• K-Means Clustering

• Logistic Regression

• Decision Trees

What does the head() function in Pandas do?

• Deletes the first row of the DataFrame

• Displays the first few rows of the DataFrame

• Returns the last few rows of the DataFrame

• Sorts the DataFrame

Which of the following is a Python library for data visualization?

• Seaborn

• Pandas

• Scikit-learn

• NumPy

In a Random Forest model, what does "out-of-bag error" refer to?

• The error generated by the validation set

• The error computed from the data that wasn't used in each individual tree's training

• The error from test data

• The error generated due to overfitting

Which of the following is a method for feature selection that penalizes large coefficients?

• Ridge Regression

• Lasso Regression

• Random Forests

• Decision Trees

In deep learning, which activation function is known to alleviate the vanishing gradient problem?

• Sigmoid

• Tanh

• ReLU

• Softmax

What is the key difference between Lasso and Ridge regression?

• Lasso uses L2 regularization, while Ridge uses L1 regularization

• Lasso can shrink coefficients to zero, while Ridge cannot

• Lasso performs better on highly correlated data

• Ridge regression penalizes the intercept term, while Lasso does not

What is the role of "Bias" in a machine learning model?

• To make predictions more accurate

• To improve the model’s ability to fit the data

• To add a constant value to the prediction

• To eliminate noise from the data