Binary classification with decision trees

Job Description:

The breast cancer dataset is a well studied binary classification dataset.

Classes: 2

Samples per class: 212(M),357(B)

Samples total:569

Dimensionality: 30

Features: real, positive

The copy of UCI ML Breast Cancer Wisconsin (Diagnostic) dataset is downloaded from: [login to view URL]

In this lab we will use the dataset to train a decision tree model.

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Read and work through all tutorial content and do all exercises below

For reference recall the following definitions

Accuracy classification score. In multilabel classification, this function computes subset accuracy: the set of labels predicted for a sample must exactly match the corresponding set of labels in y_true.

The precision is the ratio tp / (tp + fp) where tp is the number of true positives and fp the number of false positives.

The precision is intuitively the ability of the classifier not to label as positive a sample that is negative. The best value is 1 and the worst value is 0.

The recall is the ratio tp / (tp + fn) where tp is the number of true positives and fn the number of false negatives.

The recall is intuitively the ability of the classifier to find all the positive samples. The best value is 1 and the worst value is 0.

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Habilidades: Python, Processamento de dados

Sobre o Cliente:
( 14 comentários ) Greenbelt, United States

ID do Projeto: #34946402

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