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mhjensen
2019-11-07 14:00:20 +01:00
parent cfd31bac47
commit b2ba05cf10
12 changed files with 1332 additions and 29 deletions
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doc/src/DecisionTrees/DecisionTrees.do.txt
+5 -5
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@@ -2038,9 +2038,8 @@ from sklearn.preprocessing import StandardScaler
import scikitplot as skplt
from sklearn.metrics import mean_squared_error
n = 40
n_boostraps = 100
maxdegree = 8
n = 100
maxdegree = 6
# Make data set.
x = np.linspace(-3, 3, n).reshape(-1, 1)
@@ -2057,8 +2056,8 @@ X_train_scaled = scaler.transform(X_train)
X_test_scaled = scaler.transform(X_test)
for degree in range(maxdegree):
model = xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,
max_depth = maxdegree, alpha = 10, n_estimators = 10)
model = xgb.XGBRegressor(objective ='reg:squarederror', colsample_bytree = 0.3, learning_rate = 0.1,
max_depth = degree, alpha = 10, n_estimators = 10)
model.fit(X_train_scaled,y_train)
y_pred = model.predict(X_test_scaled)
polydegree[degree] = degree
@@ -2071,6 +2070,7 @@ for degree in range(maxdegree):
print('Var:', variance[degree])
print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
plt.xlim(1,maxdegree-1)
plt.plot(polydegree, error, label='Error')
plt.plot(polydegree, bias, label='bias')
plt.plot(polydegree, variance, label='Variance')
doc/src/DecisionTrees/Programs/xgregressor.py
+4 -4
View File
@@ -6,8 +6,7 @@ from sklearn.preprocessing import StandardScaler
import scikitplot as skplt
from sklearn.metrics import mean_squared_error
n = 40
n_boostraps = 100
n = 500
maxdegree = 8
# Make data set.
@@ -25,8 +24,8 @@ X_train_scaled = scaler.transform(X_train)
X_test_scaled = scaler.transform(X_test)
for degree in range(maxdegree):
model = xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,
max_depth = maxdegree, alpha = 10, n_estimators = 10)
model = xgb.XGBRegressor(objective ='reg:squarederror', colsample_bytree = 0.3, learning_rate = 0.1,
max_depth = degree, alpha = 10, n_estimators = 10)
model.fit(X_train_scaled,y_train)
y_pred = model.predict(X_test_scaled)
polydegree[degree] = degree
@@ -39,6 +38,7 @@ for degree in range(maxdegree):
print('Var:', variance[degree])
print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
plt.xlim(1,maxdegree-1)
plt.plot(polydegree, error, label='Error')
plt.plot(polydegree, bias, label='bias')
plt.plot(polydegree, variance, label='Variance')