sklearn.svm

sklearn.svm.LinearSVC

sklearn.svm.LinearSVC

This class provides Linear Support Vector Classification.

Methods

sklearn.svm.LinearSVC.fit

• sklearn.svm.LinearSVC.fit(X, y, sample_weight=None)
  
  Supported Arguments
  
  
  • X: NumPy Array or Pandas Dataframes.
  • y: NumPy Array.
  • sample_weight: Numeric NumPy Array (only if data is not distributed)

sklearn.svm.LinearSVC.predict

• sklearn.svm.LinearSVC.predict(X)
  
  Supported Arguments
  
  
  • X: NumPy Array or Pandas Dataframes.

sklearn.svm.LinearSVC.score

• sklearn.svm.LinearSVC.score(X, y, sample_weight=None)
  
  Supported Arguments
  
  
  • X: NumPy Array or Pandas Dataframes.
  • y: NumPy Array or Pandas Dataframes.
  • sample_weight: Numeric NumPy Array or Pandas Dataframes.

Example Usage:

>>> import bodo
>>> import numpy as np
>>> from sklearn.svm import LinearSVC
>>> from sklearn.preprocessing import StandardScaler
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_features=4, random_state=0)
>>> @bodo.jit
... def test_linearsvc(X, y):
...   scaler = StandardScaler()
...   scaler.fit(X)
...   X = scaler.transform(X)
...   clf = LinearSVC()
...   clf.fit(X, y)
...   ans = clf.predict(np.array([[0, 0, 0, 0]]))
...   print(ans)
...
>>> test_linearsvc(X, y)
[1]