MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is exploring a dataset with 1,000 features and only 200 samples. The goal is to build a binary classifier. Which technique should be used first during exploratory data analysis to reduce dimensionality and avoid overfitting?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use principal component analysis (PCA) to reduce dimensions.
PCA is an unsupervised dimensionality reduction technique that is well-suited for high-dimensional datasets with few samples, as it reduces features while retaining variance and helps avoid overfitting. Option A is wrong because pairwise correlation only captures linear relationships and may miss interactions, and removing correlated features may not be sufficient for high dimensionality. Option B is wrong because L1 regularization (Lasso) is a model-based feature selection method applied during training, not during initial exploratory data analysis (EDA). Option C is wrong because t-SNE is a visualization technique for reducing dimensions to 2 or 3 for plotting, but it is not suitable for generating features for modeling and can be unstable with few samples.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Compute pairwise correlations and remove highly correlated features.
Why it's wrong here
Correlation-based selection is limited.
- ✗
Apply L1 regularization (Lasso) to select features.
Why it's wrong here
Lasso is model-dependent, not pure EDA.
- ✗
Use t-SNE to visualize clusters and reduce dimensions.
Why it's wrong here
t-SNE is for visualization, not for producing features for modeling.
- ✓
Use principal component analysis (PCA) to reduce dimensions.
Why this is correct
PCA reduces dimensionality while preserving variance.
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