<gh_stars>1000+

# coding: utf-8

# # Classification Example

# In[1]:


from sklearn import datasets, metrics
import matplotlib.pyplot as plt

get_ipython().magic('matplotlib inline')


# ## Load dataset

# In[2]:


digits = datasets.load_digits()


# ## View sample image

# In[3]:


plt.figure(figsize=(3, 3))
plt.imshow(digits.images[10], cmap=plt.cm.gray_r)


# ## Actual image pixel matrix

# In[4]:


digits.images[10]


# ## Flattened vector

# In[5]:


digits.data[10]


# ## Image class label

# In[6]:


digits.target[10]


# ## Build train and test datasets

# In[7]:


X_digits = digits.data
y_digits = digits.target

num_data_points = len(X_digits)
X_train = X_digits[:int(.7 * num_data_points)]
y_train = y_digits[:int(.7 * num_data_points)]
X_test = X_digits[int(.7 * num_data_points):]
y_test = y_digits[int(.7 * num_data_points):]
print(X_train.shape, X_test.shape)


# ## Train Model

# In[8]:


from sklearn import linear_model

logistic = linear_model.LogisticRegression()
logistic.fit(X_train, y_train)


# ## Predict and Evaluate Performance

# In[9]:


print('Logistic Regression mean accuracy: %f' % logistic.score(X_test, y_test))


# # Load Wisconsin Breast Cancer Dataset

# In[10]:


import numpy as np
from sklearn.datasets import load_breast_cancer

# load data
data = load_breast_cancer()
X = data.data
y = data.target
print(X.shape, data.feature_names)


# # Partition based Clustering Example

# In[11]:


from sklearn.cluster import KMeans

km = KMeans(n_clusters=2, random_state=2)
km.fit(X)

labels = km.labels_
centers = km.cluster_centers_
print(labels[:10])


# In[12]:


from sklearn.decomposition import PCA

pca = PCA(n_components=2)
bc_pca = pca.fit_transform(X)


# In[13]:


fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))
fig.suptitle('Visualizing breast cancer clusters')
fig.subplots_adjust(top=0.85, wspace=0.5)
ax1.set_title('Actual Labels')
ax2.set_title('Clustered Labels')

for i in range(len(y)):
    if y[i] == 0:
        c1 = ax1.scatter(bc_pca[i,0], bc_pca[i,1],c='g', marker='.')
    if y[i] == 1:
        c2 = ax1.scatter(bc_pca[i,0], bc_pca[i,1],c='r', marker='.')
        
    if labels[i] == 0:
        c3 = ax2.scatter(bc_pca[i,0], bc_pca[i,1],c='g', marker='.')
    if labels[i] == 1:
        c4 = ax2.scatter(bc_pca[i,0], bc_pca[i,1],c='r', marker='.')

l1 = ax1.legend([c1, c2], ['0', '1'])
l2 = ax2.legend([c3, c4], ['0', '1'])


# # Hierarchical Clustering Example

# In[14]:


from scipy.cluster.hierarchy import dendrogram, linkage
import numpy as np
np.set_printoptions(suppress=True)

Z = linkage(X, 'ward')
print(Z)


# In[15]:


plt.figure(figsize=(8, 3))
plt.title('Hierarchical Clustering Dendrogram')
plt.xlabel('Data point')
plt.ylabel('Distance')
dendrogram(Z)
plt.axhline(y=10000, c='k', ls='--', lw=0.5)
plt.show()


# In[16]:


from scipy.cluster.hierarchy import fcluster

max_dist = 10000
hc_labels = fcluster(Z, max_dist, criterion='distance')


# In[17]:


fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))
fig.suptitle('Visualizing breast cancer clusters')
fig.subplots_adjust(top=0.85, wspace=0.5)
ax1.set_title('Actual Labels')
ax2.set_title('Hierarchical Clustered Labels')

for i in range(len(y)):
    if y[i] == 0:
        c1 = ax1.scatter(bc_pca[i,0], bc_pca[i,1],c='g', marker='.')
    if y[i] == 1:
        c2 = ax1.scatter(bc_pca[i,0], bc_pca[i,1],c='r', marker='.')
        
    if hc_labels[i] == 1:
        c3 = ax2.scatter(bc_pca[i,0], bc_pca[i,1],c='g', marker='.')
    if hc_labels[i] == 2:
        c4 = ax2.scatter(bc_pca[i,0], bc_pca[i,1],c='r', marker='.')

l1 = ax1.legend([c1, c2], ['0', '1'])
l2 = ax2.legend([c3, c4], ['1', '2'])


# # Classification Model Evaluation Metrics

# In[18]:


from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
print(X_train.shape, X_test.shape)


# In[19]:


from sklearn import linear_model

logistic = linear_model.LogisticRegression()
logistic.fit(X_train,y_train)


# ## Confusion Matrix

# In[20]:


import model_evaluation_utils as meu

y_pred = logistic.predict(X_test)
meu.display_confusion_matrix(true_labels=y_test, predicted_labels=y_pred, classes=[0, 1])


# ## True Positive, False Positive, True Negative and False Negative

# In[21]:


positive_class = 1
TP = 106
FP = 4
TN = 59
FN = 2


# ## Accuracy

# In[22]:


fw_acc = round(meu.metrics.accuracy_score(y_true=y_test, y_pred=y_pred), 5)
mc_acc = round((TP + TN) / (TP + TN + FP + FN), 5)
print('Framework Accuracy:', fw_acc)
print('Manually Computed Accuracy:', mc_acc)


# ## Precision

# In[23]:


fw_prec = round(meu.metrics.precision_score(y_true=y_test, y_pred=y_pred), 5)
mc_prec = round((TP) / (TP + FP), 5)
print('Framework Precision:', fw_prec)
print('Manually Computed Precision:', mc_prec)


# ## Recall

# In[24]:


fw_rec = round(meu.metrics.recall_score(y_true=y_test, y_pred=y_pred), 5)
mc_rec = round((TP) / (TP + FN), 5)
print('Framework Recall:', fw_rec)
print('Manually Computed Recall:', mc_rec)


# ## F1-Score

# In[25]:


fw_f1 = round(meu.metrics.f1_score(y_true=y_test, y_pred=y_pred), 5)
mc_f1 = round((2*mc_prec*mc_rec) / (mc_prec+mc_rec), 5)
print('Framework F1-Score:', fw_f1)
print('Manually Computed F1-Score:', mc_f1)


# ## ROC Curve and AUC

# In[26]:


meu.plot_model_roc_curve(clf=logistic, features=X_test, true_labels=y_test)


# # Clustering Model Evaluation Metrics

# ## Build two clustering models on the breast cancer dataset

# In[27]:


km2 = KMeans(n_clusters=2, random_state=42).fit(X)
km2_labels = km2.labels_

km5 = KMeans(n_clusters=5, random_state=42).fit(X)
km5_labels = km5.labels_


# ## Homogeneity, Completeness and V-measure

# In[28]:


km2_hcv = np.round(metrics.homogeneity_completeness_v_measure(y, km2_labels), 3)
km5_hcv = np.round(metrics.homogeneity_completeness_v_measure(y, km5_labels), 3)

print('Homogeneity, Completeness, V-measure metrics for num clusters=2: ', km2_hcv)
print('Homogeneity, Completeness, V-measure metrics for num clusters=5: ', km5_hcv)


# ## Silhouette Coefficient

# In[29]:


from sklearn import metrics

km2_silc = metrics.silhouette_score(X, km2_labels, metric='euclidean')
km5_silc = metrics.silhouette_score(X, km5_labels, metric='euclidean')

print('Silhouette Coefficient for num clusters=2: ', km2_silc)
print('Silhouette Coefficient for num clusters=5: ', km5_silc)


# ## Calinski-Harabaz Index

# In[30]:


km2_chi = metrics.calinski_harabaz_score(X, km2_labels)
km5_chi = metrics.calinski_harabaz_score(X, km5_labels)

print('Calinski-Harabaz Index for num clusters=2: ', km2_chi)
print('Calinski-Harabaz Index for num clusters=5: ', km5_chi)


# # Model tuning

# ## Build and Evaluate Default Model

# In[31]:


from sklearn.model_selection import train_test_split
from sklearn.svm import SVC

# prepare datasets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# build default SVM model
def_svc = SVC(random_state=42)
def_svc.fit(X_train, y_train)

# predict and evaluate performance
def_y_pred = def_svc.predict(X_test)
print('Default Model Stats:')
meu.display_model_performance_metrics(true_labels=y_test, predicted_labels=def_y_pred, classes=[0,1])


# ## Tune Model with Grid Search

# In[32]:


from sklearn.model_selection import GridSearchCV

# setting the parameter grid
grid_parameters = {'kernel': ['linear', 'rbf'], 
                   'gamma': [1e-3, 1e-4],
                   'C': [1, 10, 50, 100]}

# perform hyperparameter tuning
print("# Tuning hyper-parameters for accuracy\n")
clf = GridSearchCV(SVC(random_state=42), grid_parameters, cv=5, scoring='accuracy')
clf.fit(X_train, y_train)
# view accuracy scores for all the models
print("Grid scores for all the models based on CV:\n")
means = clf.cv_results_['mean_test_score']
stds = clf.cv_results_['std_test_score']
for mean, std, params in zip(means, stds, clf.cv_results_['params']):
    print("%0.5f (+/-%0.05f) for %r" % (mean, std * 2, params))
# check out best model performance
print("\nBest parameters set found on development set:", clf.best_params_)
print("Best model validation accuracy:", clf.best_score_)


# ## Evaluate Grid Search Tuned Model

# In[33]:


gs_best = clf.best_estimator_
tuned_y_pred = gs_best.predict(X_test)

print('\n\nTuned Model Stats:')
meu.display_model_performance_metrics(true_labels=y_test, predicted_labels=tuned_y_pred, classes=[0,1])


# ## Tune Model with Randomized Search

# In[34]:


import scipy
from sklearn.model_selection import RandomizedSearchCV

param_grid = {'C': scipy.stats.expon(scale=10), 
              'gamma': scipy.stats.expon(scale=.1),
              'kernel': ['rbf', 'linear']}

random_search = RandomizedSearchCV(SVC(random_state=42), param_distributions=param_grid,
                                   n_iter=50, cv=5)
random_search.fit(X_train, y_train)

print("Best parameters set found on development set:")
random_search.best_params_


# ## Evaluate Randomized Search Tuned Model

# In[35]:


rs_best = random_search.best_estimator_
rs_y_pred = rs_best.predict(X_test)
meu.get_metrics(true_labels=y_test, predicted_labels=rs_y_pred)


# # Model Interpretation

# In[36]:


from skater.core.explanations import Interpretation
from skater.model import InMemoryModel

interpreter = Interpretation(X_test, feature_names=data.feature_names)
model = InMemoryModel(logistic.predict_proba, examples=X_train, target_names=logistic.classes_)


# ## Visualize Feature Importances

# In[37]:


plots = interpreter.feature_importance.plot_feature_importance(model, ascending=False)


# ## One-way partial dependence plot

# In[38]:


p = interpreter.partial_dependence.plot_partial_dependence(['worst area'], model, grid_resolution=50, 
                                                           with_variance=True, figsize = (6, 4))


# ## Explaining Predictions

# In[39]:


from skater.core.local_interpretation.lime.lime_tabular import LimeTabularExplainer
exp = LimeTabularExplainer(X_train, feature_names=data.feature_names, 
                           discretize_continuous=True, class_names=['0', '1'])


# In[40]:


exp.explain_instance(X_test[0], logistic.predict_proba).show_in_notebook()


# In[41]:


exp.explain_instance(X_test[1], logistic.predict_proba).show_in_notebook()


# # Model Deployment

# ## Persist model to disk

# In[42]:


from sklearn.externals import joblib
joblib.dump(logistic, 'lr_model.pkl') 


# ## Load model from disk

# In[43]:


lr = joblib.load('lr_model.pkl') 
lr


# ## Predict with loaded model

# In[44]:


print(lr.predict(X_test[10:11]), y_test[10:11])


# coding: utf-8

# # Classification Example

# In[1]:


from sklearn import datasets, metrics
import matplotlib.pyplot as plt

get_ipython().magic('matplotlib inline')


# ## Load dataset

# In[2]:


digits = datasets.load_digits()


# ## View sample image

# In[3]:


plt.figure(figsize=(3, 3))
plt.imshow(digits.images[10], cmap=plt.cm.gray_r)


# ## Actual image pixel matrix

# In[4]:


digits.images[10]


# ## Flattened vector

# In[5]:


digits.data[10]


# ## Image class label

# In[6]:


digits.target[10]


# ## Build train and test datasets

# In[7]:


X_digits = digits.data
y_digits = digits.target

num_data_points = len(X_digits)
X_train = X_digits[:int(.7 * num_data_points)]
y_train = y_digits[:int(.7 * num_data_points)]
X_test = X_digits[int(.7 * num_data_points):]
y_test = y_digits[int(.7 * num_data_points):]
print(X_train.shape, X_test.shape)


# ## Train Model

# In[8]:


from sklearn import linear_model

logistic = linear_model.LogisticRegression()
logistic.fit(X_train, y_train)


# ## Predict and Evaluate Performance

# In[9]:


print('Logistic Regression mean accuracy: %f' % logistic.score(X_test, y_test))


# # Load Wisconsin Breast Cancer Dataset

# In[10]:


import numpy as np
from sklearn.datasets import load_breast_cancer

# load data
data = load_breast_cancer()
X = data.data
y = data.target
print(X.shape, data.feature_names)


# # Partition based Clustering Example

# In[11]:


from sklearn.cluster import KMeans

km = KMeans(n_clusters=2, random_state=2)
km.fit(X)

labels = km.labels_
centers = km.cluster_centers_
print(labels[:10])


# In[12]:


from sklearn.decomposition import PCA

pca = PCA(n_components=2)
bc_pca = pca.fit_transform(X)


# In[13]:


fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))
fig.suptitle('Visualizing breast cancer clusters')
fig.subplots_adjust(top=0.85, wspace=0.5)
ax1.set_title('Actual Labels')
ax2.set_title('Clustered Labels')

for i in range(len(y)):
    if y[i] == 0:
        c1 = ax1.scatter(bc_pca[i,0], bc_pca[i,1],c='g', marker='.')
    if y[i] == 1:
        c2 = ax1.scatter(bc_pca[i,0], bc_pca[i,1],c='r', marker='.')
        
    if labels[i] == 0:
        c3 = ax2.scatter(bc_pca[i,0], bc_pca[i,1],c='g', marker='.')
    if labels[i] == 1:
        c4 = ax2.scatter(bc_pca[i,0], bc_pca[i,1],c='r', marker='.')

l1 = ax1.legend([c1, c2], ['0', '1'])
l2 = ax2.legend([c3, c4], ['0', '1'])


# # Hierarchical Clustering Example

# In[14]:


from scipy.cluster.hierarchy import dendrogram, linkage
import numpy as np
np.set_printoptions(suppress=True)

Z = linkage(X, 'ward')
print(Z)


# In[15]:


plt.figure(figsize=(8, 3))
plt.title('Hierarchical Clustering Dendrogram')
plt.xlabel('Data point')
plt.ylabel('Distance')
dendrogram(Z)
plt.axhline(y=10000, c='k', ls='--', lw=0.5)
plt.show()


# In[16]:


from scipy.cluster.hierarchy import fcluster

max_dist = 10000
hc_labels = fcluster(Z, max_dist, criterion='distance')


# In[17]:


fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))
fig.suptitle('Visualizing breast cancer clusters')
fig.subplots_adjust(top=0.85, wspace=0.5)
ax1.set_title('Actual Labels')
ax2.set_title('Hierarchical Clustered Labels')

for i in range(len(y)):
    if y[i] == 0:
        c1 = ax1.scatter(bc_pca[i,0], bc_pca[i,1],c='g', marker='.')
    if y[i] == 1:
        c2 = ax1.scatter(bc_pca[i,0], bc_pca[i,1],c='r', marker='.')
        
    if hc_labels[i] == 1:
        c3 = ax2.scatter(bc_pca[i,0], bc_pca[i,1],c='g', marker='.')
    if hc_labels[i] == 2:
        c4 = ax2.scatter(bc_pca[i,0], bc_pca[i,1],c='r', marker='.')

l1 = ax1.legend([c1, c2], ['0', '1'])
l2 = ax2.legend([c3, c4], ['1', '2'])


# # Classification Model Evaluation Metrics

# In[18]:


from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
print(X_train.shape, X_test.shape)


# In[19]:


from sklearn import linear_model

logistic = linear_model.LogisticRegression()
logistic.fit(X_train,y_train)


# ## Confusion Matrix

# In[20]:


import model_evaluation_utils as meu

y_pred = logistic.predict(X_test)
meu.display_confusion_matrix(true_labels=y_test, predicted_labels=y_pred, classes=[0, 1])


# ## True Positive, False Positive, True Negative and False Negative

# In[21]:


positive_class = 1
TP = 106
FP = 4
TN = 59
FN = 2


# ## Accuracy

# In[22]:


fw_acc = round(meu.metrics.accuracy_score(y_true=y_test, y_pred=y_pred), 5)
mc_acc = round((TP + TN) / (TP + TN + FP + FN), 5)
print('Framework Accuracy:', fw_acc)
print('Manually Computed Accuracy:', mc_acc)


# ## Precision

# In[23]:


fw_prec = round(meu.metrics.precision_score(y_true=y_test, y_pred=y_pred), 5)
mc_prec = round((TP) / (TP + FP), 5)
print('Framework Precision:', fw_prec)
print('Manually Computed Precision:', mc_prec)


# ## Recall

# In[24]:


fw_rec = round(meu.metrics.recall_score(y_true=y_test, y_pred=y_pred), 5)
mc_rec = round((TP) / (TP + FN), 5)
print('Framework Recall:', fw_rec)
print('Manually Computed Recall:', mc_rec)


# ## F1-Score

# In[25]:


fw_f1 = round(meu.metrics.f1_score(y_true=y_test, y_pred=y_pred), 5)
mc_f1 = round((2*mc_prec*mc_rec) / (mc_prec+mc_rec), 5)
print('Framework F1-Score:', fw_f1)
print('Manually Computed F1-Score:', mc_f1)


# ## ROC Curve and AUC

# In[26]:


meu.plot_model_roc_curve(clf=logistic, features=X_test, true_labels=y_test)


# # Clustering Model Evaluation Metrics

# ## Build two clustering models on the breast cancer dataset

# In[27]:


km2 = KMeans(n_clusters=2, random_state=42).fit(X)
km2_labels = km2.labels_

km5 = KMeans(n_clusters=5, random_state=42).fit(X)
km5_labels = km5.labels_


# ## Homogeneity, Completeness and V-measure

# In[28]:


km2_hcv = np.round(metrics.homogeneity_completeness_v_measure(y, km2_labels), 3)
km5_hcv = np.round(metrics.homogeneity_completeness_v_measure(y, km5_labels), 3)

print('Homogeneity, Completeness, V-measure metrics for num clusters=2: ', km2_hcv)
print('Homogeneity, Completeness, V-measure metrics for num clusters=5: ', km5_hcv)


# ## Silhouette Coefficient

# In[29]:


from sklearn import metrics

km2_silc = metrics.silhouette_score(X, km2_labels, metric='euclidean')
km5_silc = metrics.silhouette_score(X, km5_labels, metric='euclidean')

print('Silhouette Coefficient for num clusters=2: ', km2_silc)
print('Silhouette Coefficient for num clusters=5: ', km5_silc)


# ## Calinski-Harabaz Index

# In[30]:


km2_chi = metrics.calinski_harabaz_score(X, km2_labels)
km5_chi = metrics.calinski_harabaz_score(X, km5_labels)

print('Calinski-Harabaz Index for num clusters=2: ', km2_chi)
print('Calinski-Harabaz Index for num clusters=5: ', km5_chi)


# # Model tuning

# ## Build and Evaluate Default Model

# In[31]:


from sklearn.model_selection import train_test_split
from sklearn.svm import SVC

# prepare datasets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# build default SVM model
def_svc = SVC(random_state=42)
def_svc.fit(X_train, y_train)

# predict and evaluate performance
def_y_pred = def_svc.predict(X_test)
print('Default Model Stats:')
meu.display_model_performance_metrics(true_labels=y_test, predicted_labels=def_y_pred, classes=[0,1])


# ## Tune Model with Grid Search

# In[32]:


from sklearn.model_selection import GridSearchCV

# setting the parameter grid
grid_parameters = {'kernel': ['linear', 'rbf'], 
                   'gamma': [1e-3, 1e-4],
                   'C': [1, 10, 50, 100]}

# perform hyperparameter tuning
print("# Tuning hyper-parameters for accuracy\n")
clf = GridSearchCV(SVC(random_state=42), grid_parameters, cv=5, scoring='accuracy')
clf.fit(X_train, y_train)
# view accuracy scores for all the models
print("Grid scores for all the models based on CV:\n")
means = clf.cv_results_['mean_test_score']
stds = clf.cv_results_['std_test_score']
for mean, std, params in zip(means, stds, clf.cv_results_['params']):
    print("%0.5f (+/-%0.05f) for %r" % (mean, std * 2, params))
# check out best model performance
print("\nBest parameters set found on development set:", clf.best_params_)
print("Best model validation accuracy:", clf.best_score_)


# ## Evaluate Grid Search Tuned Model

# In[33]:


gs_best = clf.best_estimator_
tuned_y_pred = gs_best.predict(X_test)

print('\n\nTuned Model Stats:')
meu.display_model_performance_metrics(true_labels=y_test, predicted_labels=tuned_y_pred, classes=[0,1])


# ## Tune Model with Randomized Search

# In[34]:


import scipy
from sklearn.model_selection import RandomizedSearchCV

param_grid = {'C': scipy.stats.expon(scale=10), 
              'gamma': scipy.stats.expon(scale=.1),
              'kernel': ['rbf', 'linear']}

random_search = RandomizedSearchCV(SVC(random_state=42), param_distributions=param_grid,
                                   n_iter=50, cv=5)
random_search.fit(X_train, y_train)

print("Best parameters set found on development set:")
random_search.best_params_


# ## Evaluate Randomized Search Tuned Model

# In[35]:


rs_best = random_search.best_estimator_
rs_y_pred = rs_best.predict(X_test)
meu.get_metrics(true_labels=y_test, predicted_labels=rs_y_pred)


# # Model Interpretation

# In[36]:


from skater.core.explanations import Interpretation
from skater.model import InMemoryModel

interpreter = Interpretation(X_test, feature_names=data.feature_names)
model = InMemoryModel(logistic.predict_proba, examples=X_train, target_names=logistic.classes_)


# ## Visualize Feature Importances

# In[37]:


plots = interpreter.feature_importance.plot_feature_importance(model, ascending=False)


# ## One-way partial dependence plot

# In[38]:


p = interpreter.partial_dependence.plot_partial_dependence(['worst area'], model, grid_resolution=50, 
                                                           with_variance=True, figsize = (6, 4))


# ## Explaining Predictions

# In[39]:


from skater.core.local_interpretation.lime.lime_tabular import LimeTabularExplainer
exp = LimeTabularExplainer(X_train, feature_names=data.feature_names, 
                           discretize_continuous=True, class_names=['0', '1'])


# In[40]:


exp.explain_instance(X_test[0], logistic.predict_proba).show_in_notebook()


# In[41]:


exp.explain_instance(X_test[1], logistic.predict_proba).show_in_notebook()


# # Model Deployment

# ## Persist model to disk

# In[42]:


from sklearn.externals import joblib
joblib.dump(logistic, 'lr_model.pkl') 


# ## Load model from disk

# In[43]:


lr = joblib.load('lr_model.pkl') 
lr


# ## Predict with loaded model

# In[44]:


print(lr.predict(X_test[10:11]), y_test[10:11])
# coding: utf-8 # # Classification Example # In[1]: # ## Load dataset # In[2]: # ## View sample image # In[3]: # ## Actual image pixel matrix # In[4]: # ## Flattened vector # In[5]: # ## Image class label # In[6]: # ## Build train and test datasets # In[7]: # ## Train Model # In[8]: # ## Predict and Evaluate Performance # In[9]: # # Load Wisconsin Breast Cancer Dataset # In[10]: # load data # # Partition based Clustering Example # In[11]: # In[12]: # In[13]: # # Hierarchical Clustering Example # In[14]: # In[15]: # In[16]: # In[17]: # # Classification Model Evaluation Metrics # In[18]: # In[19]: # ## Confusion Matrix # In[20]: # ## True Positive, False Positive, True Negative and False Negative # In[21]: # ## Accuracy # In[22]: # ## Precision # In[23]: # ## Recall # In[24]: # ## F1-Score # In[25]: # ## ROC Curve and AUC # In[26]: # # Clustering Model Evaluation Metrics # ## Build two clustering models on the breast cancer dataset # In[27]: # ## Homogeneity, Completeness and V-measure # In[28]: # ## Silhouette Coefficient # In[29]: # ## Calinski-Harabaz Index # In[30]: # # Model tuning # ## Build and Evaluate Default Model # In[31]: # prepare datasets # build default SVM model # predict and evaluate performance # ## Tune Model with Grid Search # In[32]: # setting the parameter grid # perform hyperparameter tuning # view accuracy scores for all the models # check out best model performance # ## Evaluate Grid Search Tuned Model # In[33]: # ## Tune Model with Randomized Search # In[34]: # ## Evaluate Randomized Search Tuned Model # In[35]: # # Model Interpretation # In[36]: # ## Visualize Feature Importances # In[37]: # ## One-way partial dependence plot # In[38]: # ## Explaining Predictions # In[39]: # In[40]: # In[41]: # # Model Deployment # ## Persist model to disk # In[42]: # ## Load model from disk # In[43]: # ## Predict with loaded model # In[44]: