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Data Science for Lingerie Design
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Module 6
Classification Models for Curating Top Picks
1
Predicting Photoshoot-Worthy Lingerie with Naive Bayes
Apply the Naive Bayes classifier to predict if a new lingerie piece is 'photoshoot-worthy' based on its categorical features (e.g., fabric, color, type).
2
Building and Interpreting CART Decision Trees for Lingerie Selection
Build a CART decision tree for lingerie selection using the Gini index and interpret the resulting decision rules.
3
CART vs. C4.5 Tree Structure Comparison
Compare the structure of a CART tree with a C4.5 tree (using information gain) built from the same lingerie dataset.
4
Classifying Lingerie with K-Nearest Neighbors (K=3)
Use a K-Nearest Neighbors (K=3) model to classify a new lingerie piece based on the majority class of its neighbors in a feature space.
5
Decision Tree Bias-Variance Trade-off in Lingerie Selection
Evaluate the bias-variance trade-off by comparing the performance of a simple decision tree (high bias) vs. a complex one (high variance) on a lingerie selection task.
Previous module
Causal Inference to Avoid Design Fallacies
Next module
Experimental Design for Comparing Lingerie Styles