XG Boost Classifier
XG Boost is a pretty famous machine learning algorithm introduced in 2012. XG Boost is a gradient boosting decision tree algorithm. Instead of training a large ensemble of trees at once (like in the Random Forest Algorithm), XG Boost will train the group of trees one by one, calculating the error after building each tree, then building the next tree to reduce the previous mistake. The final predictor will be an average of all the trees.
In this notebook, I will be looking at the famous breastcancer dataset. This dataset is a multi-class classification problem, where I need to predict the correct target for each observation from a range of possible classes. We will attempt to predict the proper target class using this model, given the feature of each type of class, I often reuse this dataset between my tree-based notebooks. Using the same dataset makes it very easy to compare and contrast the performance of different tree-based models, and keep the trees a reasonable size.
Dataset
Breast Cancer Dataset: https://www.kaggle.com/hdza1991/breast-cancer-wisconsin-data-set
Import Preliminaries¶
%matplotlib inline
%config InlineBackend.figure_format='retina'
# Import modules
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn
import warnings
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV
from xgboost import XGBClassifier
# Set pandas options
pd.set_option('max_columns',1000)
pd.set_option('max_rows',30)
pd.set_option('display.float_format', lambda x: '%.3f' % x)
# Set plotting options
mpl.rcParams['figure.figsize'] = (9.0, 3.0)
# Set warning options
warnings.filterwarnings('ignore');
Import Data¶
# Import Breast Cancer data
breast_cancer = load_breast_cancer()
X, y = breast_cancer.data, breast_cancer.target
# Conduct a train-test split on the data
train_x, test_x, train_y, test_y = train_test_split(X,y)
# View the training dataframe
pd.DataFrame(train_x, columns=breast_cancer['feature_names']).head(5)
Data Overview¶
# Plot a barplot of the target clasees
pd.Series(train_y).value_counts().plot.barh(grid=False,
color=['#B2E2E2','#66C2A4'],
width=0.25,edgecolor='w')
plt.title('Target Outcomes')
plt.ylabel('Class')
plt.xlabel('Measure of Disease Progression');
Fit the Model¶
# Fit the intial model
xgb_model = XGBClassifier(n_estimators=100, n_jobs=-1)
xgb_model.fit(train_x, train_y);
Model Evaluation¶
Cross Validation Score¶
# View the cross validation score of the intial model
scores = cross_val_score(xgb_model, train_x, train_y, cv=10,
scoring='accuracy')
print(f'Cross Validation Score: {scores.mean():.5f}')
Confustion Matrix¶
# Training Confusion Matrix
from sklearn.metrics import confusion_matrix
cmatrix = pd.DataFrame(confusion_matrix(train_y, xgb_model.predict(train_x)))
cmatrix.index.name = 'class'
cmatrix['result'] = 'actual'
cmatrix.set_index('result', append=True, inplace=True)
cmatrix = cmatrix.reorder_levels(['result', 'class'])
cmatrix = cmatrix.stack()
cmatrix = pd.DataFrame(cmatrix)
cmatrix.columns = ['prediction']
cmatrix.unstack()
Feature Importance¶
# Plot Tree's Feature Importance
plt.figure(figsize=(10,5))
n_features = breast_cancer.data.shape[1]
plt.barh(range(n_features), xgb_model.feature_importances_, align='center',
color='#4D977E')
plt.yticks(np.arange(n_features), breast_cancer.feature_names)
plt.title('XGBoost Feature Importance')
plt.xlabel("Feature importance")
plt.ylabel("Features")
plt.ylim(-1, n_features);
plt.xlim(0,0.25)
Parameter Tuning¶
# Define paraameter range and score lists
n_estimators_range = np.arange(0,300, 25)[1:]
train_score = []
test_score = []
# Train a knn_model for every neighbour value in our list
for i in n_estimators_range:
xgb_model=XGBClassifier(n_estimators = i, n_jobs=-1).fit(train_x,train_y)
train_score.append(cross_val_score(xgb_model, train_x,
train_y, cv=10,
scoring='accuracy').mean())
test_score.append(cross_val_score(xgb_model, test_x,
test_y, cv=10,
scoring='accuracy').mean())
# Plot our results
mpl.rcParams['figure.figsize'] = (9.0, 6.0)
plt.plot(n_estimators_range,train_score,label="Train",linewidth=2, color='#66C2A4')
plt.plot(n_estimators_range,test_score,label="Test", linewidth=2,linestyle='--', color='#B2E2E2')
plt.legend()
plt.title('Random Forest Model')
plt.xlabel('Number of Estimators')
plt.ylabel('Accuracy');
Grid Searching Turnning¶
# Set up parameter grid
grid = {'n_estimators':np.arange(0,100, 25)[1:],
'learning_rate': [0.01, 0.025, 0.05,0.005],
'max_depth':list(range(2,30,4)),
'booster': ['gbtree','gblinear','dart'],
'reg_alpha':[0,1]}
# Conduct gird search
grid_search = GridSearchCV(estimator=xgb_model, param_grid=grid,
scoring='accuracy', n_jobs=-1, refit=True, cv=10,
return_train_score=True)
# Fit model
grid_search.fit(train_x,train_y);
# Print out the parameter for the best score
print('Accuracy of best parameters: %.5f'%grid_search.best_score_)
print('Best parameters: %s' %grid_search.best_params_)
Final Model¶
# Fit the final model
xgb_model = XGBClassifier(booster='gbtree', learning_rate=0.05,
max_depth=6, n_estimators = 75)
xgb_model.fit(train_x, train_y)
# View the cross validation score of the intial model
scores = cross_val_score(xgb_model, train_x, train_y, cv=10,
scoring='accuracy')
print(f'Cross Validation Score: {scores.mean():.5f}')
Confustion Matrix¶
# Training confusion matrix
from sklearn.metrics import confusion_matrix
cmatrix = pd.DataFrame(confusion_matrix(train_y, xgb_model.predict(train_x)))
cmatrix.index.name = 'class'
cmatrix['result'] = 'actual'
cmatrix.set_index('result', append=True, inplace=True)
cmatrix = cmatrix.reorder_levels(['result', 'class'])
cmatrix = cmatrix.stack()
cmatrix = pd.DataFrame(cmatrix)
cmatrix.columns = ['prediction']
cmatrix.unstack()
Feature Importance¶
# Plot ensembles's feature importance
plt.figure(figsize=(10,5))
n_features = breast_cancer.data.shape[1]
plt.barh(range(n_features), xgb_model.feature_importances_, align='center',
color='#4D977E')
plt.yticks(np.arange(n_features), breast_cancer.feature_names)
plt.title('Random Forest Feature Importance')
plt.xlabel("Feature importance")
plt.ylabel("Features")
plt.ylim(-1, n_features)
plt.xlim(0,0.25);
Predict Results¶
# Predict the results from our test data
pd.Series(rf_model.predict(test_x)).head(n=7)
General Notes¶
-- Trees are built in sequentially order so XGboost takes longer to train
-- XGBoost uses a loss function when computing the next tree, you have the abiltiy to change the loss function
-- You are building many trees and final results are an average of the trees
-- You can also apply l1 and l2 regularization to algorithim
-- XGBoost does not use entropy to leave splits but use a Gain instead
Sources¶
Author: Kavi Sekhon
