cv = ShuffleSplit(n_splits=100, test_size=0.33, random_state=0)
ADB = AdaBoostClassifier(
DecisionTreeClassifier(max_depth=2),
n_estimator=grid_search.best_params['n_estimators'],
learning_rate=grid_search.best_params_['learning_rate'],
random_state=0)
plot_learning_curve(ADB, title1, X, y, ylim=(0.4, 1.01), cv=cv, n_jobs=4)
# In[7]:
#Draw the validation curve
title2 = "Validation Curve with Adaboost "
xlabel = "n_estimators"
ylabel = "Score"
ADB = AdaBoostClassifier(
DecisionTreeClassifier(max_depth=2),
learning_rate=grid_search.best_params_['learning_rate'],
random_state=0)
plot_validation_curve(ADB,
title2,
xlabel,
ylabel,
X,
y,
param_name='n_estimators',
ylim=None,
cv=cv,
n_jobs=4,
param_range=np.arange(1, 200, 20))
plt.show()
grid_search.param_grid['n_neighbors'][::-1])
plt.xlabel('weights')
plt.ylabel('n_neighbors')
# In[6]:
#Draw learning curve
X, y = arr_in, arr_out
title1 = "Learning Curve (K Nearest Neighbors)"
cv = StratifiedKFold(arr_out, n_folds=3)
estimator = KNeighborsClassifier()
plot_learning_curve(estimator, title1, X, y, ylim=None, cv=cv, n_jobs=4)
# In[7]:
#Draw validation curve
title2 = "Validation Curve with K Nearest Neighbors "
xlabel = "n_neighbors"
ylabel = "Score"
plot_validation_curve(estimator,
title2,
xlabel,
ylabel,
X,
y,
param_name='n_neighbors',
ylim=None,
cv=cv,
n_jobs=1,
param_range=np.arange(1, 10, 1))
plt.show()
plt.xlabel('min_samples_split')
plt.ylabel('max_depth')
# In[6]:
# Draw the learning curve
X, y = arr_in, arr_out
title1 = "Learning Curves (Decision Tree Classifier)"
cv = ShuffleSplit(n_splits=100, test_size=0.33, random_state=0)
estimator = DecisionTreeClassifier()
plot_learning_curve(estimator, title1, X, y, ylim=(0.4, 1.01), cv=cv, n_jobs=4)
# In[7]
# Draw the validation curve
title2 = "Validation Curve with Decision Tree Classifier "
xlabel = "max_depth"
ylabel = "Score"
plot_validation_curve(estimator,
title2,
xlabel,
ylabel,
X,
y,
param_name='max_depth',
ylim=None,
cv=cv,
n_jobs=1,
param_range=np.arange(1, max_d))
plt.show()