Ungraded Lab: Logistic Regression using Scikit-Learn¶
Dataset¶
Let's start with the same dataset as before.
In [1]:
Copied!
import numpy as np
X = np.array([[0.5, 1.5], [1,1], [1.5, 0.5], [3, 0.5], [2, 2], [1, 2.5]])
y = np.array([0, 0, 0, 1, 1, 1])
import numpy as np
X = np.array([[0.5, 1.5], [1,1], [1.5, 0.5], [3, 0.5], [2, 2], [1, 2.5]])
y = np.array([0, 0, 0, 1, 1, 1])
Fit the model¶
The code below imports the logistic regression model from scikit-learn. You can fit this model on the training data by calling fit function.
In [2]:
Copied!
from sklearn.linear_model import LogisticRegression
lr_model = LogisticRegression()
lr_model.fit(X, y)
from sklearn.linear_model import LogisticRegression
lr_model = LogisticRegression()
lr_model.fit(X, y)
Out[2]:
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None, max_iter=100,
multi_class='auto', n_jobs=None, penalty='l2',
random_state=None, solver='lbfgs', tol=0.0001, verbose=0,
warm_start=False)
Make Predictions¶
You can see the predictions made by this model by calling the predict function.
In [3]:
Copied!
y_pred = lr_model.predict(X)
print("Prediction on training set:", y_pred)
y_pred = lr_model.predict(X)
print("Prediction on training set:", y_pred)
Prediction on training set: [0 0 0 1 1 1]
Calculate accuracy¶
You can calculate this accuracy of this model by calling the score function.
In [4]:
Copied!
print("Accuracy on training set:", lr_model.score(X, y))
print("Accuracy on training set:", lr_model.score(X, y))
Accuracy on training set: 1.0
In [ ]:
Copied!
In [ ]:
Copied!