第十五周 —— sklearn练习

最新推荐文章于 2024-09-09 11:06:22 发布

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本文通过一个随机生成的二分类问题，对比了朴素贝叶斯、支持向量机及随机森林三种机器学习算法的表现。采用10折交叉验证进行训练与测试，并通过准确率、F1分数及AUC-ROC等指标评估了每种算法的性能。

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Assignment
In the second ML assignment you have to compare the performance of
three di↵erent classification algorithms, namely Naive Bayes, SVM, and
Random Forest.
For this assignment you need to generate a random binary classification
problem, and then train and test (using 10-fold cross validation) the three
algorithms. For some algorithms inner cross validation (5-fold) for choosing
the parameters is needed. Then, show the classification performace
(per-fold and averaged) in the report, and briefly discussing the results.
Note
The report has to contain also a short description of the methodology used
to obtain the results.
Dragone, Passerini (DISI) Scikit-Learn Machine Learning 20 / 22

Steps
1、 Create a classification dataset (n samples ! 1000, n features ! 10)
2 、Split the dataset using 10-fold cross validation
3 、Train the algorithms
  GaussianNB
  SVC (possible C values [1e-02, 1e-01, 1e00, 1e01, 1e02], RBF kernel)
  RandomForestClassifier (possible n estimators values [10, 100, 1000])
4、 Evaluate the cross-validated performance
  Accuracy
  F1-score
  AUC ROC

5 、Write a short report summar

from sklearn import datasets  
from sklearn import cross_validation  
from sklearn.naive_bayes import GaussianNB  
from sklearn.svm import SVC  
from sklearn.ensemble import RandomForestClassifier  
from sklearn import metrics  
  
dataset = datasets.make_classification(n_samples=2000, n_features=10,  
            n_informative=2, n_redundant=2, n_repeated=0, n_classes=2)  
  
# spilt using 10-fold  
kf = cross_validation.KFold(1000, n_folds=10, shuffle=True)  
for train_index, test_index in kf:  
    X_train, y_train = dataset[0][train_index], dataset[1][train_index]  
    X_test, y_test = dataset[0][test_index], dataset[1][test_index]  
  

# Evaluate the cross-validated performance  
print("Evaluate the cross-validated performance:")  
acc = metrics.accuracy_score(y_test, pred)  
print("Accuracy: ", acc)  
f1 = metrics.f1_score(y_test, pred)  
print("F1-score: ",f1)  
auc = metrics.roc_auc_score(y_test, pred)  
print("AUC ROC: ", auc)  
print("\n")  
  
# SVC  
clf = SVC(C=1e-01, kernel='rbf', gamma=0.1)  
clf.fit(X_train, y_train)  
pred = clf.predict(X_test)  
print("SVC: ")  
print("pred: \n", pred)  
print("y_test: \n", y_test)  
# Evaluate the cross-validated performance  
print("Evaluate the cross-validated performance:")  
acc = metrics.accuracy_score(y_test, pred)  
print("Accuracy: ", acc)  
f1 = metrics.f1_score(y_test, pred)  
print("F1-score: ",f1)  
auc = metrics.roc_auc_score(y_test, pred)  
print("AUC ROC: ", auc)  
print("\n")  
  

# Gaussian Naive Bayes  
clf = GaussianNB()  
clf.fit(X_train, y_train)  
pred = clf.predict(X_test)  
print("GaussianNB:")  
print("pred: \n", pred)  
print("y_test: \n", y_test)  



# Evaluate the cross-validated performance  
print("Evaluate the cross-validated performance:")  
acc = metrics.accuracy_score(y_test, pred)  
print("Accuracy: ", acc)  
f1 = metrics.f1_score(y_test, pred)  
print("F1-score: ",f1)  
auc = metrics.roc_auc_score(y_test, pred)  
print("AUC ROC: ", auc)  
print("\n")  


# Random Forest  
clf = RandomForestClassifier(n_estimators=6)  
clf.fit(X_train, y_train)  
pred = clf.predict(X_test)  
print("RandomForestClassifier: ")  
print("pred: \n", pred)  
print("y_test: \n", y_test)  