A data scientist is developing a churn prediction model using a decision tree algorithm. The dataset includes a continuous feature, 'Customer Age', which has high cardinality and a skewed distribution. The initial model is overfitting, likely due to the creation of complex splits based on insignificant age variations. To mitigate this, the data scientist decides to apply binning to the 'Customer Age' feature. Which binning strategy is most effective at creating meaningful groups that adapt to the natural distribution of customer ages and improve the model's generalization?
The correct answer is quantile-based binning. This method divides the continuous data into intervals with an equal number of observations, making it robust to skewed distributions and outliers. By ensuring each bin is equally populated, it creates more meaningful and balanced splits for the decision tree, which can help reduce overfitting and improve model generalization.
Equal-width binning is incorrect because it creates bins of the same size range. With skewed data, this can result in some bins having many observations while others are nearly empty, which does not solve the issue of creating meaningful splits for the model.
Applying a Box-Cox transformation is incorrect because its primary purpose is to stabilize variance and transform data to be more normally distributed, not to discretize it or reduce its cardinality. The goal is to group ages, not just change the shape of the distribution.
Directly one-hot encoding a high-cardinality continuous variable is highly inappropriate. It would create a very large and sparse feature matrix, dramatically increasing dimensionality and likely worsening model performance and overfitting, a problem known as the 'curse of dimensionality'.
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Why is Quantile-based binning effective for skewed data?
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How does Equal-width binning compare to Quantile-based binning for skewed datasets?
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What problems arise from one-hot encoding a high-cardinality continuous feature?