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Created 3 years ago
# Jovian Commit Essentials
# Please retain and execute this cell without modifying the contents for `jovian.commit` to work
!pip install jovian --upgrade -q
import jovian
jovian.set_project('adaboost-cancer-prediction')
jovian.set_colab_id('1qbkgENw9CZ4pbqJ7u7_Hxhd8U5zJRUae')In this lab, you’ll explore the breast cancer dataset and try to train the model to predict if the person is having breast cancer or not. We will start off with a weak learner, a decision tree with maximum depth = 2.
We will then build an adaboost ensemble with 50 trees with a step of 3 and compare the performance with the weak learner.
Let's get started by loading the libraries.
import numpy as np
import pandas as pd
import sklearn
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.datasets import load_breast_cancer
from sklearn.datasets import load_digits
from sklearn import metrics
%matplotlib inline
import os
import warnings
warnings.filterwarnings('ignore')
We will use the breast cancer dataset in which the target variable has 1 if the person has cancer and 0 otherwise. Let's load the data.
cancer = load_breast_cancer()
digits = load_digits()
data = cancer