# Split the dataset into test and train sets
from sklearn.model_selection import train_test_split as skl_tts
ind_train_set, ind_test_set, dep_train_set, dep_test_set = skl_tts(
ind_var, dep_var, test_size=0.25)
# Fit the model
from sklearn.naive_bayes import GaussianNB as skl_gn
classifier = skl_gn()
classifier.fit(ind_train_set, dep_train_set)
# Predict the test set
prediction = classifier.predict(ind_test_set)
# Create confusion matrix to verify the results
from sklearn.metrics import confusion_matrix as skl_cm
conf_matrix = skl_cm(y_true=dep_test_set, y_pred=prediction)
conf_matrix
# =============================================
# Disclaimer: All the plotting code below is not mine.
# Font: Machine Learning A-Z™: Hands-On Python & R In Data Science - kernel_svm.py source code
# =============================================
from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
import numpy as np
X_set, y_set = ind_train_set, dep_train_set
X1, X2 = np.meshgrid(
np.arange(start=X_set[:, 0].min() - 1,
stop=X_set[:, 0].max() + 1,
step=0.01),
np.arange(start=X_set[:, 1].min() - 1,
lin_classifier.fit(ind_train_set, dep_train_set) rbf_classifier.fit(ind_train_set, dep_train_set) sig_classifier.fit(ind_train_set, dep_train_set) pol_classifier.fit(ind_train_set, dep_train_set) # Predict the test set lin_prediction = lin_classifier.predict(ind_test_set) rbf_prediction = rbf_classifier.predict(ind_test_set) sig_prediction = sig_classifier.predict(ind_test_set) pol_prediction = pol_classifier.predict(ind_test_set) # Create confusion matrix to verify the results from sklearn.metrics import confusion_matrix as skl_cm print 'Linear Kernel confusion matrix' skl_cm(y_true = dep_test_set, y_pred = lin_prediction) print 'RBF (Gaussian) Kernel confusion matrix' skl_cm(y_true = dep_test_set, y_pred = rbf_prediction) print 'Sigmoid Kernel confusion matrix' skl_cm(y_true = dep_test_set, y_pred = sig_prediction) print 'Polynomial (3degree) Kernel confusion matrix' skl_cm(y_true = dep_test_set, y_pred = pol_prediction) # ============================================= # Disclaimer: All the plotting code below is not mine. # Font: Machine Learning A-Z™: Hands-On Python & R In Data Science - kernel_svm.py source code # ============================================= from matplotlib.colors import ListedColormap import matplotlib.pyplot as plt import numpy as np
ind_var, dep_var, test_size=0.25)
# Fit the model
from sklearn.tree import DecisionTreeClassifier as skl_dtc
classifier = skl_dtc(
criterion='entropy', # Could be gini
splitter='best',
max_depth=5)
classifier.fit(ind_train_set, dep_train_set)
# Predict the test set
prediction = classifier.predict(ind_test_set)
# Create confusion matrix to verify the results
from sklearn.metrics import confusion_matrix as skl_cm
skl_cm(y_true=dep_test_set, y_pred=prediction)
# =============================================
# Disclaimer: All the plotting code below is not mine.
# Font: Machine Learning A-Z™: Hands-On Python & R In Data Science - kernel_svm.py source code
# =============================================
from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
import numpy as np
X_set, y_set = ind_train_set, dep_train_set
X1, X2 = np.meshgrid(
np.arange(start=X_set[:, 0].min() - 1,
stop=X_set[:, 0].max() + 1,
step=0.01),
np.arange(start=X_set[:, 1].min() - 1,
def confusion_matrix(output, target):
with torch.no_grad():
pred = torch.argmax(output, dim=1)
result = skl_cm(target.detach().cpu().numpy(), pred.detach().cpu().numpy())
return result