Hello all.
Here is some basic question to revise your concepts.
Q1: How does the k-Nearest Neighbors algorithm classify a new data point?
a) By finding the closest data point and taking its label
b) By averaging the labels of the k nearest data points
c) By fitting a decision boundary
d) By using gradient descent
Q2: In the context of Linear Regression, what does the coefficient of determination (R-squared) measure?
a) The correlation between independent variables
b) The strength of the relationship between dependent and independent variables
c) The proportion of variance in the dependent variable explained by the independent variables
d) The total number of data points
Q3: What is the primary purpose of a Decision Tree in machine learning?
a) Clustering data
b) Classifying data
c) Reducing dimensionality
d) Calculating probabilities
Q4: Which technique is commonly used to measure the impurity of nodes in a Decision Tree?
a) Cross-Validation
b) Gini Index
c) Principal Component Analysis
d) L1 Regularization
All the best guys.