Python LogisticRegression.predict示例

示例#1

文件： linear_models.py 项目： ParticularJ/MLAlgorithms

def classification():
    # Generate a random binary classification problem.
    X, y = make_classification(n_samples=1000, n_features=100,
                               n_informative=75, random_state=1111,
                               n_classes=2, class_sep=2.5, )
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1,
                                                        random_state=1111)

    model = LogisticRegression(lr=0.01, max_iters=500, penalty='l1', C=0.01)
    model.fit(X_train, y_train)
    predictions = model.predict(X_test)
    print('classification accuracy', accuracy(y_test, predictions))

示例#2

文件： linear_models.py 项目： satpreetsingh/MLAlgorithms

def classification():
    X, y = make_classification(
        n_samples=1000,
        n_features=100,
        n_informative=75,
        random_state=1111,
        n_classes=2,
        class_sep=2.5,
    )
    X_train, X_test, y_train, y_test = train_test_split(X,
                                                        y,
                                                        test_size=0.1,
                                                        random_state=1111)
    model = LogisticRegression(lr=0.01, max_iters=500, penalty='l1', C=0.01)
    model.fit(X_train, y_train)
    predictions = model.predict(X_test)
    print('classification accuracy', accuracy(y_test, predictions))

示例#3

文件： linear_models.py 项目： zy20091082/MLAlgorithms

def classification():
    # Generate a random binary classification problem.
    X, y = make_classification(n_samples=1000,
                               n_features=100,
                               n_informative=75,
                               random_state=1111,
                               n_classes=2,
                               class_sep=2.5)
    X_train, X_test, y_train, y_test = train_test_split(X,
                                                        y,
                                                        test_size=0.1,
                                                        random_state=1111)

    model = LogisticRegression(lr=0.01, max_iters=500, penalty="l1", C=0.01)
    model.fit(X_train, y_train)
    predictions = model.predict(X_test)
    print("classification accuracy", accuracy(y_test, predictions))

示例#4

文件： pca.py 项目： eostb/AI-Algorithms

except ImportError:
    from sklearn.cross_validation import train_test_split
from sklearn.datasets import make_classification

from mla.linear_models import LogisticRegression
from mla.metrics import accuracy
from mla.pca import PCA

# logging.basicConfig(level=logging.DEBUG)

# Generate a random binary classification problem.
X, y = make_classification(n_samples=1000, n_features=100, n_informative=75,
                           random_state=1111, n_classes=2, class_sep=2.5, )

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25,
                                                    random_state=1111)

for s in ['svd', 'eigen']:
    p = PCA(15, solver=s)

    # fit PCA with training data, not entire dataset
    p.fit(X_train)
    X_train_reduced = p.transform(X_train)
    X_test_reduced = p.transform(X_test)

    model = LogisticRegression(lr=0.001, max_iters=2500)
    model.fit(X_train_reduced, y_train)
    predictions = model.predict(X_test_reduced)
    print('Classification accuracy for %s PCA: %s'
          % (s, accuracy(y_test, predictions)))

示例#5

文件： test_classification_accuracy.py 项目： vijaynitrr/MLAlgorithms

def test_linear_model_classification():
    model = LogisticRegression(lr=0.01, max_iters=500, penalty='l1', C=0.01)
    model.fit(X_train, y_train)
    predictions = model.predict(X_test)
    assert roc_auc_score(y_test, predictions) >= 0.95

示例#6

文件： test_classification_accuracy.py 项目： ParticularJ/MLAlgorithms

def test_linear_model():
    model = LogisticRegression(lr=0.01, max_iters=500, penalty='l1', C=0.01)
    model.fit(X_train, y_train)
    predictions = model.predict(X_test)
    assert roc_auc_score(y_test, predictions) >= 0.95

示例#7

文件： pca.py 项目： ParticularJ/MLAlgorithms

    from sklearn.cross_validation import train_test_split
from sklearn.datasets import make_classification

from mla.linear_models import LogisticRegression
from mla.metrics import accuracy
from mla.pca import PCA

# logging.basicConfig(level=logging.DEBUG)

# Generate a random binary classification problem.
X, y = make_classification(n_samples=1000, n_features=100, n_informative=75,
                           random_state=1111, n_classes=2, class_sep=2.5, )


X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25,
                                                        random_state=1111)

for s in ['svd', 'eigen']:
    p = PCA(15, solver=s)

    # fit PCA with training data, not entire dataset
    p.fit(X_train)
    X_train_reduced = p.transform(X_train)
    X_test_reduced = p.transform(X_test)
    
    model = LogisticRegression(lr=0.001, max_iters=2500)
    model.fit(X_train_reduced, y_train)
    predictions = model.predict(X_test_reduced)
    print('Classification accuracy for %s PCA: %s'
          % (s, accuracy(y_test, predictions)))

示例#8

文件： pca.py 项目： bradparks/MLAlgorithms___Minimal-and-clean-examples-of-machine-learning-algorithms-Edit

from mla.metrics import accuracy
from mla.pca import PCA

# logging.basicConfig(level=logging.DEBUG)

# Generate a random binary classification problem.
X, y = make_classification(
    n_samples=1000,
    n_features=100,
    n_informative=75,
    random_state=1111,
    n_classes=2,
    class_sep=2.5,
)

for s in ['svd', 'eigen']:
    p = PCA(15, solver=s)
    p.fit(X)
    X = p.transform(X)
    print(X.shape)

    X_train, X_test, y_train, y_test = train_test_split(X,
                                                        y,
                                                        test_size=0.25,
                                                        random_state=1111)
    model = LogisticRegression(lr=0.001, max_iters=2500)
    model.fit(X_train, y_train)
    predictions = model.predict(X_test)
    print('Classification accuracy for %s PCA: %s' %
          (s, accuracy(y_test, predictions)))