Ch3. Scikit-learn

<Scikit-learn>

Scikit-learn은 ML 코딩을 할 때 유용하게 쓸 수 있는 라이브러리이다

Scikit-learn에서 제공하는 대표적인 기능들은 다음과 같다

 

• useful dataset load

from sklearn import datasets
import numpy as np

iris = datasets.load_iris()
X = iris.data[:, [2, 3]]
y = iris.target

print('Class labels:', np.unique(y))

 

 

• train, test split

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=1, stratify=y)

 

 

• data standardization

from sklearn.preprocessing import StandardScaler

sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)

 

• using various ML model

 

 

 

<Perceptron with Scikit-learn>

Perceptron 학습

from sklearn.linear_model import Perceptron

ppn = Perceptron(eta0=0.1, random_state=1)
ppn.fit(X_train_std, y_train)

 

 

Acc 출력

from sklearn.metrics import accuracy_score

print('Accuracy: %.3f' % accuracy_score(y_test, y_pred))
// print('Accuracy: %.3f' % ppn.score(X_test_std, y_test))

 

 

학습 결과

Iris dataset