Practice
1. What will be the output shape of the following code snippet using K-Fold cross-validation with 5 splits on a dataset of 100 samples?
from sklearn.model_selection import KFold
kf = KFold(n_splits=5)
for train_index, test_index in kf.split(range(100)):
print(len(test_index))easy
Solution
Step 1: Calculate the number of samples per fold.
100 samples divided by 5 splits results in 20 samples per fold.Step 2: Eliminate wrong options.
25 samples per fold (B) is incorrect, 6 outputs (C) is incorrect, 4 outputs (D) is incorrect.Final Answer:
Option A -> Option AQuick Check:
100 samples, 5 splits = 20 per fold. [OK]
Hint: 100 samples, 5 splits = 20 per fold. [OK]
Common Mistakes:
2. In the context of handling class imbalance, which technique is specifically designed to adjust the loss function to focus more on hard-to-classify examples?
easy
Solution
Step 1: Identify the technique that modifies the loss function.
Focal Loss is designed to focus more on hard-to-classify examples by adjusting the loss function.Step 2: Eliminate wrong options.
Oversampling and undersampling adjust the dataset, not the loss function. Cost-sensitive learning modifies the cost, not the focus on hard examples.Final Answer:
Option D -> Option DQuick Check:
Focal Loss focuses on hard examples. [OK]
Hint: Focal Loss focuses on hard examples. [OK]
Common Mistakes:
3. You notice that your XGBoost model is overfitting despite using early stopping. What could be the root cause?
medium
Solution
Step 1: Identify overfitting causes
Overfitting can occur if the model is too complex, such as having trees with high max depth.Step 2: Eliminate wrong options
The learning rate is too low, causing the model to converge slowly. is incorrect because a low learning rate would not cause overfitting. The number of boosting rounds is too low, leading to underfitting. is incorrect because too few rounds would cause underfitting, not overfitting. The regularization parameters are set too high, restricting model complexity. is incorrect because high regularization would prevent overfitting, not cause it.Final Answer:
Option A -> Option AQuick Check:
High max depth = Overfitting risk [OK]
Hint: High max depth = Overfitting risk [OK]
Common Mistakes:
4. A KNN model is performing poorly on a high-dimensional dataset. What is the most likely cause?
medium
Solution
Step 1: Consider high-dimensional effects
In high dimensions, distance metrics often become less effective.Step 2: Eliminate wrong options
The model has too few neighbors. is incorrect as increasing neighbors may not help. The dataset is too small. is wrong because size isn't the main issue. The model is overfitting due to too many neighbors. is incorrect as overfitting is less likely with many neighbors.Final Answer:
Option B -> Option BQuick Check:
High dimensions dilute distance metrics. [OK]
Hint: High dimensions dilute distance metrics. [OK]
Common Mistakes:
5. You are tasked with designing a CNN for a medical imaging application where interpretability is crucial. Which approach would best leverage the inductive bias of CNNs for this task?
hard
Solution
Step 1: Consider interpretability needs
Small kernels with more layers help capture local patterns, which are crucial for interpretability in medical imaging.Step 2: Eliminate wrong options
Use very deep networks to capture complex patterns is incorrect because very deep networks can be less interpretable. Use shallow networks with large kernels to capture global patterns is incorrect as large kernels reduce local detail capture. Use networks with attention mechanisms to highlight important regions is incorrect because attention mechanisms, while useful, are not leveraging CNN's inductive bias.Final Answer:
Option A -> Option AQuick Check:
Small kernels capture local details, aiding interpretability. [OK]
Hint: Small kernels capture local details, aiding interpretability. [OK]
Common Mistakes: