python-scikit-learnAdaboost classifier model usage example
from sklearn import datasets, ensemble, model_selection
X, y = datasets.load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)
model = ensemble.AdaBoostClassifier()
model.fit(X_train, y_train)
score = model.score(X_test, y_test)ctrl + cfrom sklearn importimport module from scikit-learn | model_selection.train_test_splitsplits given |
load_irisloads Iris dataset | .AdaBoostClassifier()create AdaBoost model |
.fit(train model with a given features and target variable dataset | scoretrained model accuracy score for a given (test) dataset |
Usage example
from sklearn import datasets, ensemble, model_selection
X, y = datasets.load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)
model = ensemble.AdaBoostClassifier()
model.fit(X_train, y_train)
print(model.score(X_test, y_test))output
0.9736842105263158
More of Python Scikit Learn
- Using OneClassSVM for outliers detection example
- Decision tree classifier example
- Feature selection using KBest and chi2
- Using Ridge regression model example
- Grid search CV example
- Using quadratic linear regression
- Convert bunch object to dataframe
- Using quantile regression
- Yeo Johnson transformation example
- Feature importance example
See more codes...