After running PCA on a dataset, you find that: Principal Component 1 (PC1) explains 40% of the variance PC2 explains 30% PC3 explains 15% PC4 explains 10% PC5 explains 5% If your goal is to keep components that explain at least 90% of the total variance, how many principal components should you retain?

In soft voting, ensemble predictions are made by:

Why are the new features created by PCA often harder to interpret?

Assume we are building a regression model using a neural network to predict house prices. Which of the following loss functions is most appropriate?

Which of the following is the most general term that includes machine learning, deep learning, and large language models?

Assume we run Lasso with a penalty weight of 0.01, and the model includes nearly all variables. If we want the model to become more selective and include fewer variables, what should we do with the penalty weight?

What does the kernel size parameter control in a convolutional layer?

Suppose you’ve already fit a model with model.fit(X_train, y_train). What does model.predict(X_test) return?

Which of the following is an example of an unsupervised learning method?

In scikit-learn, what does setting fit_intercept=True do in linear models?