Which step should a machine learning specialist take to remove features that are irrelevant for the analysis and reduce the model’s complexity?
Plot a histogram of the features and compute their standard deviation. Remove features with high variance.
Plot a histogram of the features and compute their standard deviation. Remove features with low variance.
Build a heatmap showing the correlation of the dataset against itself. Remove features with low mutual correlation scores.
Run a correlation check of all features against the target variable. Remove features with low target variable correlation scores.
Explanations:
Plotting a histogram and removing features based on high variance may not be the best strategy. Features with high variance could still be important for predicting the target variable.
Removing features with low variance is not the right approach here. Features with low variance may still be relevant for the prediction, and removing them could result in losing useful information.
Building a heatmap to show correlation between features helps identify relationships among predictors but does not help in evaluating their relationship with the target variable.
Running a correlation check of all features against the target variable helps identify which features are most relevant for predicting the target (sale price). Features with low correlation to the target should be removed to reduce model complexity.
It seems to me that the answer is:
Run a correlation check of all features against the target variable. Remove features with low target variable correlation scores.
I value that the answer is:
Run a correlation check of all features against the target variable. Remove features with low target variable correlation scores.