def balance_class_balance(path="images/class_balance.png"):
data = load_game()
y = data["outcome"]
oz = ClassBalance(labels=["draw", "loss", "win"])
oz.fit(y)
return oz.poof(outpath=path)
def balance_class_balance(path="images/class_balance.png"):
data = load_game()
y = data["outcome"]
oz = ClassBalance(labels=["draw", "loss", "win"])
oz.fit(y)
return oz.poof(outpath=path)
def compare_class_balance(path="images/class_balance_compare.png"):
data = load_occupancy()
features = ["temperature", "relative_humidity", "light", "C02", "humidity"]
classes = ['unoccupied', 'occupied']
# Extract the numpy arrays from the data frame
X = data[features]
y = data["occupancy"]
# Create the train and test data
_, _, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Instantiate the classification model and visualizer
visualizer = ClassBalance(labels=classes)
visualizer.fit(y_train, y_test)
return visualizer.poof(outpath=path)
def compare_class_balance(path="images/class_balance_compare.png"):
data = load_occupancy()
features = ["temperature", "relative_humidity", "light", "C02", "humidity"]
classes = ['unoccupied', 'occupied']
# Extract the numpy arrays from the data frame
X = data[features]
y = data["occupancy"]
# Create the train and test data
_, _, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Instantiate the classification model and visualizer
visualizer = ClassBalance(labels=classes)
visualizer.fit(y_train, y_test)
return visualizer.poof(outpath=path)
else:
o[(index * n_ngrams) + i] = features
bs = bs[~np.all(bs == 0, axis=1)]
o = o[~np.all(o == 0, axis=1)]
binding_sites = bs
other = o
binding_sites_labels = np.ones(binding_sites.shape[0], dtype=np.uint8)
other_labels = np.zeros(other.shape[0], dtype=np.uint8)
X = np.concatenate((binding_sites, other))
y = np.concatenate((binding_sites_labels, other_labels))
# %%
visualizer = ClassBalance(labels=class_names)
visualizer.fit(y)
visualizer.poof()
# %%
visualizer = ParallelCoordinates()
visualizer.fit_transform(X, y)
visualizer.poof()
# %%
visualizer = Rank1D()
visualizer.fit(X, y)
visualizer.transform(X)
visualizer.poof()
# %%
visualizer = Rank2D()
visualizer.fit_transform(X)
def visualizeClassImbalance(labels_train, lables_test=None):
visualizer = ClassBalance(labels=["boring", "interesting"])
visualizer.fit(labels_train, lables_test)
visualizer.poof()
def class_balance(classes, y):
from yellowbrick.target import ClassBalance
visualizer = ClassBalance(labels=classes)
visualizer.fit(y)
visualizer.poof()
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, presort='deprecated',
random_state=0, splitter='best')
viz = FeatureImportances(dt)
viz.fit(X_train, y_train)
viz.show();
from yellowbrick.classifier import ROCAUC
visualizer = ROCAUC(rf, classes = ['stayed','quit'])
visualizer.fit(X_train, y_train)
visualizer.score(X_test, y_test)
visualizer.poof();
from yellowbrick.classifier import ROCAUC
visualizer = ROCAUC(dt, classes = ['stayed','quit'])
visualizer.fit(X_train, y_train)
visualizer.score(X_test, y_test)
visualizer.poof();
"""### So, I can sayb the Random Forest Classifier performed better in this dataset.
Thanks for Checking this out!
---
def draw_class_balance(self):
visualizer = ClassBalance(labels=self.le.classes_)
visualizer.fit(self.training_labels)
visualizer.poof()