Feature selection methods in ML Python - Model Metrics & Evaluation

Metrics & Evaluation - Feature selection methods

Which metric matters for Feature Selection and WHY

Feature selection helps pick the most useful data parts for a model. The key metrics to check are model accuracy, precision, and recall after selecting features. These show if the chosen features help the model make better predictions.

Also, watch model training time and complexity. Good feature selection reduces these, making the model faster and simpler.

Confusion Matrix Example After Feature Selection

      Actual \ Predicted | Positive | Negative
      -------------------|----------|---------
      Positive           |    80    |   20
      Negative           |    10    |   90
    

From this matrix:

• True Positives (TP) = 80
• False Positives (FP) = 10
• True Negatives (TN) = 90
• False Negatives (FN) = 20

Precision = 80 / (80 + 10) = 0.89

Recall = 80 / (80 + 20) = 0.80

These numbers show how well the model performs with the selected features.

Precision vs Recall Tradeoff in Feature Selection

Choosing features affects precision and recall differently:

• High precision means fewer false alarms. Useful when false positives are costly, like spam filters.
• High recall means catching most real cases. Important in health checks, like cancer detection.

Feature selection can improve one but hurt the other. For example, removing features might reduce false positives (better precision) but miss some true cases (lower recall).

Good vs Bad Metric Values for Feature Selection

Good:

• Accuracy above baseline (better than random guessing)
• Precision and recall balanced and high (e.g., both above 0.8)
• Reduced training time and simpler model

Bad:

• Accuracy close to random (e.g., 50% for binary)
• Very low precision or recall (below 0.5)
• Model complexity remains high despite feature selection

Common Pitfalls in Feature Selection Metrics

• Accuracy paradox: High accuracy can hide poor recall or precision if classes are imbalanced.
• Data leakage: Using future or test data features can falsely boost metrics.
• Overfitting: Selecting features that fit training data noise leads to poor real-world results.
• Ignoring metric tradeoffs: Focusing only on accuracy without checking precision and recall can mislead.

Self-Check Question

Your model after feature selection has 98% accuracy but only 12% recall on the positive class (e.g., fraud). Is it good for production? Why or why not?

Answer: No, it is not good. The very low recall means the model misses most positive cases (fraud). Even with high accuracy, the model fails to catch important cases, which is critical in fraud detection.

Key Result

Feature selection improves model performance by balancing accuracy, precision, recall, and reducing complexity.

Practice

1. Which of the following best describes the purpose of feature selection in machine learning?

easy

A. To choose the most important features to improve model performance

B. To increase the number of features in the dataset

C. To randomly remove features from the dataset

D. To convert features into labels for training

Solution

1. Step 1: Understand feature selection goal

   Feature selection aims to pick the most useful features that help the model learn better.

2. Step 2: Evaluate options

   Only To choose the most important features to improve model performance correctly states that feature selection chooses important features to improve model performance.

3. Final Answer:

   To choose the most important features to improve model performance -> Option A

4. Quick Check:

   Feature selection = pick important features [OK]

Hint: Feature selection picks useful features, not random or all [OK]

Common Mistakes:

• Thinking feature selection adds features
• Confusing feature selection with feature engineering
• Believing feature selection changes labels

2. Which Python library provides the SelectKBest feature selection method?

easy

A. pandas

B. scikit-learn

C. numpy

D. matplotlib

Solution

1. Step 1: Recall common ML libraries

   Scikit-learn is the main library for machine learning tools including feature selection.

2. Step 2: Match method to library

   SelectKBest is part of scikit-learn's feature_selection module, not pandas, numpy, or matplotlib.

3. Final Answer:

   scikit-learn -> Option B

4. Quick Check:

   SelectKBest = scikit-learn [OK]

Hint: SelectKBest is from scikit-learn, not data or plotting libs [OK]

Common Mistakes:

• Choosing pandas because it handles data
• Confusing numpy with ML feature tools
• Selecting matplotlib which is for plotting

3. What will be the output shape of features after applying VarianceThreshold(threshold=0.1) on a dataset with shape (100, 5) where only 3 features have variance above 0.1?

medium

A. (5, 100)

B. (100, 5)

C. (3, 100)

D. (100, 3)

Solution

1. Step 1: Understand VarianceThreshold effect

   VarianceThreshold removes features with variance below the threshold, keeping only those above it.

2. Step 2: Apply to given data

   Since 3 features have variance above 0.1, only those 3 remain. The number of samples (100) stays the same.

3. Final Answer:

   (100, 3) -> Option D

4. Quick Check:

   VarianceThreshold keeps features with variance > threshold [OK]

Hint: Output shape keeps rows, columns = features passing threshold [OK]

Common Mistakes:

• Confusing rows and columns in shape
• Assuming all features remain
• Thinking variance threshold changes sample count

4. Consider this code snippet:

from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression

model = LogisticRegression()
rfe = RFE(model, n_features_to_select=2)
rfe.fit(X, y)
selected = rfe.transform(X)
print(selected.shape)

If X has shape (50, 4), but the output shape is (50, 4), what is the likely error?

medium

A. RFE does not reduce features automatically

B. n_features_to_select is greater than number of features

C. RFE was not fitted before transform

D. LogisticRegression model is incompatible with RFE

Solution

1. Step 1: Understand RFE usage

   RFE must be fitted before calling transform to reduce features.

2. Step 2: Check given code and output

   If output shape is unchanged, likely transform was called before fitting or fitting failed.

3. Step 3: Identify cause

   Since code shows fitting before transform, but output shape unchanged, the most common cause is that transform was called on unfitted RFE or fit did not complete properly.

4. Final Answer:

   RFE was not fitted before transform -> Option C

5. Quick Check:

   Fit RFE before transform to reduce features [OK]

Hint: Ensure RFE is fitted before transform [OK]

Common Mistakes:

• Assuming transform always reduces features without fitting
• Ignoring the need to fit RFE
• Thinking model type causes shape issue

5. You have a dataset with 10 features, but 4 are highly correlated and 2 have very low variance. Which feature selection approach best improves model simplicity and speed?

hard

A. Apply VarianceThreshold to remove low variance, then use correlation filter to drop correlated features

B. Use RFE with all features and keep all 10

C. Use SelectKBest to pick top 6 features by univariate scores

D. Randomly drop 4 features to reduce dimensionality

Solution

1. Step 1: Identify problem features

   Low variance features add little info; correlated features add redundancy.

2. Step 2: Choose method to remove both

   VarianceThreshold removes low variance features; correlation filter removes redundant correlated features.

3. Step 3: Evaluate options

   Apply VarianceThreshold to remove low variance, then use correlation filter to drop correlated features combines both methods to improve simplicity and speed effectively.

4. Final Answer:

   Apply VarianceThreshold to remove low variance, then use correlation filter to drop correlated features -> Option A

5. Quick Check:

   Remove low variance + correlated features = simpler model [OK]

Hint: Combine variance and correlation filters for best feature reduction [OK]

Common Mistakes:

• Using only one method ignoring other feature issues
• Randomly dropping features without reason
• Keeping all features with RFE without reduction