from sklearn.datasets import load_iris
iris_dataset = load_iris()
iris_dataset.feature_names
['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']
iris_dataset.target
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
X_feature = iris_dataset.data
Y_target = iris_dataset.target
X_feature[-2:-1]
array([[6.2, 3.4, 5.4, 2.3]])
X_new = [[3,5,4,1],[5,3,4,2],[1,2,3,4],[5.9, 3. , 5.1, 1.8]]
from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression()
logreg.fit(X_feature,Y_target)
D:\Users\kevin.zhang\Anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
LogisticRegression()
logreg.predict(X_new)
array([0, 1, 2, 2])
import pickle as pkl
persist_model = pkl.dumps(logreg)
persist_model
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import joblib
joblib.dump(logreg, 'logreg.pkl')
['logreg.pkl']
new_logreg_estimator = joblib.load('logreg.pkl')
new_logreg_estimator.predict(X_new)
array([0, 1])
