(.env) [boris@fedora34server YANDEXQ]$ cat predict.py
import pandas
from sklearn import model_selection
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv"
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
dataframe = pandas.read_csv(url, names=names)
array = dataframe.values
X = array[:,0:8]
Y = array[:,8]
test_size = 0.33
seed = 7
X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y, test_size=test_size, random_state=seed)
model = LogisticRegression(solver='lbfgs', max_iter=120)
model.fit(X_train, Y_train)
predicted = model.predict(X_test)
report = classification_report(Y_test, predicted)
print(report)
(.env) [boris@fedora34server YANDEXQ]$ python predict.py
precision recall f1-score support
0.0 0.81 0.88 0.84 162
1.0 0.74 0.63 0.68 92
accuracy 0.79 254
macro avg 0.78 0.75 0.76 254
weighted avg 0.78 0.79 0.78 254
(.env) [boris@fedora34server YANDEXQ]$ cat regres01.py
import pandas
from sklearn import model_selection
from sklearn.linear_model import LinearRegression
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.data"
names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
dataframe = pandas.read_csv(url, delim_whitespace=True, names=names)
array = dataframe.values
X = array[:,0:13]
Y = array[:,13]
seed = 7
kfold = \ model_selection.KFold(n_splits=10,shuffle=True,random_state=seed)
model = LinearRegression()
scoring = 'neg_mean_absolute_error'
results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring)
print(("MAE: %.3f (%.3f)") % (results.mean(), results.std()))
(.env) [boris@fedora34server YANDEXQ]$ python regres01.py
MAE: -3.387 (0.667)
(.env) [boris@fedora34server YANDEXQ]$ cat regres02.py
# Cross Validation Regression MSE
import pandas
from sklearn import model_selection
from sklearn.linear_model import LinearRegression
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.data"
names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
dataframe = pandas.read_csv(url, delim_whitespace=True, names=names)
array = dataframe.values
X = array[:,0:13]
Y = array[:,13]
seed = 7
kfold = \ model_selection.KFold(n_splits=10,shuffle=True,random_state=seed)
model = LinearRegression()
scoring = 'neg_mean_squared_error'
results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring)
print(("MSE: %.3f (%.3f)") % (results.mean(), results.std()))
(.env) [boris@fedora34server YANDEXQ]$ python regres02.py
MSE: -23.747 (11.143)
(.env) [boris@fedora34server YANDEXQ]$ cat regres03.py
import pandas
from sklearn import model_selection
from sklearn.linear_model import LinearRegression
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.data"
names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
dataframe = pandas.read_csv(url, delim_whitespace=True, names=names)
array = dataframe.values
X = array[:,0:13]
Y = array[:,13]
seed = 7
kfold \ = model_selection.KFold(n_splits=10,shuffle=True,random_state=seed)
model = LinearRegression()
scoring = 'r2'
results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring)
print(("R^2: %.3f (%.3f)") % (results.mean(), results.std()))
(.env) [boris@fedora34server YANDEXQ]$ python regres03.py
R^2: 0.718 (0.099)
References
https://machinelearningmastery.com/metrics-evaluate-machine-learning-algorithms-python/
