def main():
use_feature_index = [2, 3]
iris = datasets.load_iris()
X = iris.data[:, use_feature_index]
y = iris.target
feature_names = np.array(iris.feature_names)[use_feature_index]
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.3,
random_state=0)
n_estimators = 50
rf = RandomForest(n_estimators=n_estimators, random_state=300)
rf.fit(X_train, y_train)
score_m = rf.score(X_test, y_test)
# scoreの出力
print("-" * 50)
print("score:" + str(score_m))
# 特徴量の重要度の出力
print("-" * 50)
f_importance_m = rf.feature_importances
print("feature importances:")
for f_name, f_importance in zip(feature_names, f_importance_m):
print(" ", f_name, ":", f_importance)
# 決定した領域の出力
plt = PlotResult(rf, X_train, y_train, X_test, y_test, feature_names,
"my_random_forest")
plt.plot_result()
def analyze_RF(X, y):
X_train, X_test, y_train, y_test = train_test_split(X, y)
arr = list(range(50, 150, 10))
scores = []
for e in arr:
model = RandomForest(num_trees=e, num_features=3)
model.fit(X_train, y_train)
scores.append(model.score(X_test, y_test))
fig, ax = plt.subplots()
ax.plot(arr, scores)
return scores, arr
