7. Model Evaluation

Activity 7.01: Final Test Project

1. Import the relevant libraries:

   import pandas as pd 
   import numpy as np 
   import json 
   %matplotlib inline 
   import matplotlib.pyplot as plt 
   from sklearn.preprocessing import OneHotEncoder 
   from sklearn.model_selection import RandomizedSearchCV, train_test_split
   from sklearn.ensemble import GradientBoostingClassifier 
   from sklearn.metrics import (accuracy_score, precision_score, \
   recall_score, confusion_matrix, precision_recall_curve)

2. Read the breast-cancer-data.csv dataset:

   data = pd.read_csv('../Datasets/breast-cancer-data.csv')
   data.info() 

3. Let's separate the input data (X) and the target (y):

   X = data.drop(columns=['diagnosis'])
   y = data['diagnosis'].map({'malignant': 1, 'benign': 0}.get).values

4. Split the dataset into training and test sets:

   X_train, X_test, \
   y_train, y_test = train_test_split(X, y, \
            ...