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Python辅助 | STAT3612 Homework 3

使用Python分析恶性肿瘤数据

STAT3612 Homework 3:
Sumbit (in the ipynb format) via Moodle before 11:59pm December 12, 2019.
(Breast Cancer Classification Tasks). Use the load_breast_cancer() from sklearn.datasets
to get a copy of the breast cancer (diagnostic) dataset with 569 samples with 212 Malignant
and 357 Benign cases. Consider only the first 10 attributes (mean features) as the predictor
variables and perform the following tasks.
Step 1. (20%) Fit a decision tree classifier with max_depth =3. Visualize the fitted tree by
export_graphviz. Report the training accuracy.
Step 2. (20%) Fit the random forests and gradient boosting machines. Report the training
accuracy for both models.
Step 3. (20%) Fit support vector classifiers with linear and RBF kernels. Report the training
accuracy for both models.
Step 4. (20%) Fit a muti-layer perceptron (MLP) classifier. Report the training accuracy.
Step 5. (20%) Pick the most-accurate model (likely a black-box model) from the above
model fits. Run the post-hoc interpretability analysis.
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