Supervised Learning Algorithms Flashcards
7 cards from real MS-DS Master of Data science practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Supervised Learning Algorithms flashcards as text
In a support vector machine, what is the role of the kernel trick?
Answer: It maps data into a higher-dimensional space to find a linear separator
The kernel trick implicitly maps input features to a higher-dimensional space where a linear decision boundary can separate classes that are non-linearly separable in the original space.
Which loss function is most appropriate for a multi-class classification problem with mutually exclusive classes?
Answer: Categorical cross-entropy
Categorical cross-entropy measures the difference between the predicted probability distribution and the one-hot encoded true class label across all mutually exclusive classes.
What does the 'max_depth' hyperparameter control in a decision tree?
Answer: The longest path from the root to a leaf node
max_depth limits the longest path from the root to any leaf, controlling model complexity and preventing overfitting by restricting how deep the tree can grow.
A logistic regression model outputs a probability of 0.3 for the positive class. With a threshold of 0.5, how is this classified?
Answer: Negative class, because 0.3 < 0.5
With a decision threshold of 0.5, any predicted probability below 0.5 is assigned to the negative class, so 0.3 maps to the negative class.
Which of the following best describes the bias-variance tradeoff in supervised learning?
Answer: High-bias models underfit the training data; high-variance models overfit it
High bias means the model is too simple and underfits, while high variance means the model is too complex and overfits, capturing noise in the training data.
In k-nearest neighbors (k-NN) regression, what happens as k increases?
Answer: The decision boundary becomes smoother and the model becomes less complex
Larger k averages over more neighbors, producing smoother predictions and reducing variance, but potentially increasing bias by over-smoothing local structure.
Which regularization technique randomly drops neurons during training to prevent overfitting in neural networks?
Answer: Dropout
Dropout randomly sets a fraction of neuron activations to zero during each training step, forcing the network to learn redundant representations and reducing co-adaptation.