【发布时间】：2019-11-21 15:55:31
【问题描述】：
几点说明：
Python 版本：Python 3.5.0
Sklearn 版本：0.20.3
我正在使用的 sklearn 包中有一个MLPRegressor，它取得了相当不错的效果。
我正在运行的代码如下：
from sklearn.neural_network import MLPRegressor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler
from sklearn import preprocessing
import pandas as pd, numpy as np
import sklearn
def compare_values(arr1, arr2):
thediff = 0
thediffs = []
for thing1, thing2 in zip(arr1, arr2):
thediff = abs(thing1 - thing2)
thediffs.append(thediff)
return thediffs
def robustscale(data):
scaler = RobustScaler()
df_scaled = pd.DataFrame(scaler.fit_transform(data), columns=data.columns)
return df_scaled
total_avgs = []
def driver(data, labels, model, scaling):
best_model = None
best = 1000000
avgs = []
for x in range(5):
X_train, X_test, y_train, y_test = train_test_split(data, label, shuffle=True, test_size = 0.2)
model.fit(X_train, y_train)
preds = model.predict(X_test)
differences = np.average(compare_values(y_test, preds))
print("CURRENT MODEL Average: {}".format(differences))
if differences > best:
best_model = model
avgs.append(differences)
total_avgs.append(differences)
print("Average Performance Overall: {}".format(np.average(avgs)))
print("Best Performance Overall: {}".format(np.min(avgs)))
data = pd.read_csv('new.csv')
# handle some data manipulation. Dropping columns and such. Nothing important
data = data
rb_data = robustscale(data)
mlp = MLPRegressor(
activation = 'tanh',
hidden_layer_sizes = (1000, 1000, 1000),
alpha = 0.009,
learning_rate = 'invscaling',
learning_rate_init = 0.01,
max_iter = 200,
momentum = 0.9,
solver = 'lbfgs',
warm_start = False
)
print("############################################")
print("NOW TESTING ROBUST SCALE DATA: ")
driver(rb_data, label, mlp, "rb")
print("############################################")
print("\n")
print("BEST MODEL PERFORMANCE: {}".format(np.min(total_avgs)))
我试图理解为什么我在回归问题上得到了如此好的结果。
我的 MLP 是这样配置的（使用 GridSearchCV 后选择的参数）
mlp = MLPRegressor(
activation = 'tanh',
hidden_layer_sizes = (1000, 1000, 1000),
alpha = 0.009,
learning_rate = 'invscaling',
learning_rate_init = 0.01,
max_iter = 200,
momentum = 0.9,
solver = 'lbfgs',
warm_start = True
)
（是的，我也觉得很奇怪 relu 没有被选中。但它从来没有被选中）
当我设置warm_start = True 时，我得到如下输出：
############################################
NOW TESTING ROBUST SCALE DATA:
CURRENT MODEL Average: 21.163831505120193
CURRENT MODEL Average: 12.44361687293673
CURRENT MODEL Average: 5.687720697116947
CURRENT MODEL Average: 4.225979713815092
CURRENT MODEL Average: 5.235999000929669
Average Performance Overall: 9.751429557983725
Best Performance Overall: 4.225979713815092
############################################
显然，每次运行的性能都在提高。
但是，当我设置 warm_start = False 时，我得到：
############################################
NOW TESTING ROBUST SCALE DATA:
CURRENT MODEL Average: 25.221720858740714
CURRENT MODEL Average: 20.3609370299473
CURRENT MODEL Average: 23.385534335200845
CURRENT MODEL Average: 21.89668702232435
CURRENT MODEL Average: 15.38606220618026
Average Performance Overall: 21.250188290478693
Best Performance Overall: 15.38606220618026
############################################
很明显，warm_start = True 正在以积极的方式影响性能。但是如何？在循环的每次运行中，我都会随机重新拆分我的数据，创建一个全新的模型，然后运行测试。新模式如何向旧模式学习？
【问题讨论】：
-
来自the docs，您正在创建一个新模型，但告诉回归器“重用之前调用的解决方案以适应初始化”。抱歉，但目前尚不完全清楚您要澄清什么
-
我也阅读了文档。我想我不明白的是引用的行。 new 模型如何知道有关先前实例化的任何信息？假设，如果我运行 1000 次，我的数据不会只是过度拟合，而不是那么调整吗？我想我不明白新模型如何从以前的模型中学习，以及过度拟合是如何产生的
标签： python machine-learning scikit-learn