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Now, create a set of x-values, using np.linspace() function, with a range from -10 to 10 and 100 data points. Store the result in the variable x.
Next, we use the logistic function to calculate $y$ values (Store the result in the variable y), and finally, we use matplotlib.pyplot to plot the $x$,$y$ values.
The resulting plot will show the S-shaped curve of the logistic function, which is useful for modeling probabilities.
Use StandardScaler to standardize the features and store the results in the variables X_train_scaler and X_test_scaler.
y_pred = logreg.predict(---)
print("Accuracy:", logreg.score(---, ---))
from sklearn.datasets import make_blobs
X, y = make_blobs(n_samples=1000, centers=2,
random_state=0, cluster_std=0.85)
For the model set random_state = 0.
Round to three decimal places
from sklearn.datasets import make_blobs
X, y = make_blobs(n_samples=1000, centers=2,
random_state=49, cluster_std=1.95)
For the model set random_state = 0.
Test dataset:
X_test= [[0.77499332, 5.10445441],
[2.33615249, 3.733497 ],
[3.53929109, 1.13492994],
[2.82894249, 4.00864077],
[4.61800816, 2.39809546]]
y_test=[0, 1, 1, 0, 0]
Round to two decimal places
