Python AdaBoostClassifier.fitted示例

from sklearn import datasets
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import AdaBoostClassifier
from sklearn.metrics import confusion_matrix, accuracy_score

data = datasets.load_iris("path/to/the/dataset")

data.features = data[[
    "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"
]]
data.targets = data.Class

X_train, X_test, y_train, y_test = train_test_split(data.features,
                                                    data.targets,
                                                    test_size=0.25,
                                                    random_state=42)

model = AdaBoostClassifier(n_estimators=100, learning_rate=1, random_state=133)
model.fitted = model.fit(X_train, y_train)
model.predictions = model.fitted.predict(X_test)

print(confusion_matrix(y_test, model.predictions))
print(accuracy_score(y_test, model.predictions))

示例#2

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文件： adaboost.py 项目： michelleduer/independent-study

import pandas as pd
from sklearn.ensemble import AdaBoostClassifier
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.cross_validation import train_test_split

data = pd.read_csv("C:\\Users\\User\\Desktop\\iris_data.csv")

#print(data)
data.features = data[["SepalLength","SepalWidth","PetalLength","PetalWidth"]]
data.targets = data.Class 

feature_train, feature_test, target_train, target_test = train_test_split(data.features, data.targets, test_size=.2)

model = AdaBoostClassifier(n_estimators=100,learning_rate=1,random_state=123)
model.fitted = model.fit(feature_train, target_train)
model.predictions = model.fitted.predict(feature_test)

print(confusion_matrix(target_test, model.predictions))
print(accuracy_score(target_test, model.predictions))

示例#3

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文件： Boosting.py 项目： SabirKhanAkash/Intro-to-ML-and-DL-with-Python

import pandas as pd
from sklearn.ensemble import AdaBoostClassifier
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split

data = pd.read_csv(
    "D:\Study Materials\Study\My Python Workspace\Intro to ML DL with Python\Boosting\iris_data.csv"
)

data.features = data[[
    "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"
]]
data.targets = data.Class

feature_train, feature_test, target_train, target_test = train_test_split(
    data.features, data.targets, test_size=0.2)

model = AdaBoostClassifier(n_estimators=100, learning_rate=1, random_state=123)
model.fitted = model.fit(feature_train, target_train)
model.predictions = model.fitted.predict(feature_test)

print("\nThe confusion Matrix: \n",
      confusion_matrix(target_test, model.predictions))
print("\nAccuracy: ", accuracy_score(target_test, model.predictions))