from lemon.datasets import load_iris from lemon.model_utils.model_selection import train_test_split from lemon.model_utils.metrics import accuracy from lemon.supervised.naive_bayes import GaussianNB, MultinomialNB import numpy as np x, y = load_iris(x_y=True) x = np.round(x) train_x, test_x, train_y, test_y = train_test_split(x, y) mnb = MultinomialNB().fit(train_x, train_y) pred = mnb.predict(train_x) print(accuracy(train_y, pred)) gnb = GaussianNB().fit(train_x, train_y) pred = gnb.predict(train_x) print(accuracy(train_y, pred))
from lemon.datasets import load_iris from lemon.model_utils.model_selection import train_test_split from lemon.supervised.naive_bayes import GaussianNB from lemon.model_utils.metrics import accuracy x, y = load_iris(x_y=True) x_train, x_test, y_train, y_test = train_test_split(x, y, split_rate=0.8, random_state=2020) model = GaussianNB() model.fit(x_train, y_train) pred = model.predict(x_test) print(accuracy(y_test, pred))
from lemon.datasets import load_iris from lemon.model_utils.model_selection import train_test_split from lemon.model_utils.metrics import accuracy from lemon.supervised.linear_model import LogisticRegression x, y = load_iris(x_y=True) x, y = x[y < 2], y[y < 2] train_x, test_x, train_y, test_y = train_test_split(x, y) model = LogisticRegression().fit(train_x, train_y) pred = model.predict(test_x) print(accuracy(test_y, pred))
from lemon.supervised.linear_model import Perceptron from lemon.datasets import load_breast_cancer from lemon.model_utils.metrics import accuracy import numpy as np x, y = load_breast_cancer(x_y=True) y = np.where(y > 0, 1, -1) train_x, test_x = x[:400], x[400:] train_y, test_y = y[:400], y[400:] print(test_x) model = Perceptron(verbose=True) model.fit(train_x, train_y) pred_y = model.predict(test_x) print(accuracy(test_y, pred_y)) from sklearn.linear_model.perceptron import Perceptron as P m = P(verbose=True) m.fit(train_x, train_y) p = model.predict(test_x) print(accuracy(test_y, p))