案例：红酒数据集分析
2024-05-12 03:23:47
数据来源：https://www.kaggle.com/datasets/uciml/red-wine-quality-cortez-et-al-2009
红酒数据集一共有1599个样本，12个特征。其中11个为红酒的理化性质，quality列为红酒的品质（10分制）。
首先导入需要的库，加载数据集
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('D:\\Py_dataset\\winequality-red.csv',sep = ';')
df.head()
| fixed acidity | volatile acidity | citric acid | residual sugar | chlorides | free sulfur dioxide | total sulfur dioxide | density | pH | sulphates | alcohol | quality | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7.4 | 0.70 | 0.00 | 1.9 | 0.076 | 11.0 | 34.0 | 0.998 | 3.51 | 0.56 | 9.4 | 5 |
| 1 | 7.8 | 0.88 | 0.00 | 2.6 | 0.098 | 25.0 | 67.0 | 0.997 | 3.20 | 0.68 | 9.8 | 5 |
| 2 | 7.8 | 0.76 | 0.04 | 2.3 | 0.092 | 15.0 | 54.0 | 0.997 | 3.26 | 0.65 | 9.8 | 5 |
| 3 | 11.2 | 0.28 | 0.56 | 1.9 | 0.075 | 17.0 | 60.0 | 0.998 | 3.16 | 0.58 | 9.8 | 6 |
| 4 | 7.4 | 0.70 | 0.00 | 1.9 | 0.076 | 11.0 | 34.0 | 0.998 | 3.51 | 0.56 | 9.4 | 5 |
12个字段，具体信息如下：
| No | 属性 | 数据类型 | 字段描述 |
|---|---|---|---|
| 1 | fixed acidity | Numeric | 非挥发性酸 |
| 2 | volatile acidity | Numeric | 挥发性酸 |
| 3 | citric acid | Numeric | 柠檬酸 |
| 4 | residual sugar | Numeric | 残糖 |
| 5 | chlorides | Numeric | 氯化物 |
| 6 | free sulfur dioxide | Numeric | 游离二氧化硫 |
| 7 | total sulfur dioxide | Numeric | 总二氧化硫 |
| 8 | density | Numeric | 密度 |
| 9 | pH | Numeric | 酸碱度 |
| 10 | sulphates | Numeric | 硫酸盐 |
| 11 | alcohol | Numeric | 酒精 |
| 12 | quality (score between 0 and 10) | Numeric | 葡萄酒质量（1-10之间） |
数据探索及可视化
df.shape # (1599, 12)
df.info() # 没有缺失值
'''# Column Non-Null Count Dtype
--- ------ -------------- ----- 0 fixed acidity 1599 non-null float641 volatile acidity 1599 non-null float642 citric acid 1599 non-null float643 residual sugar 1599 non-null float644 chlorides 1599 non-null float645 free sulfur dioxide 1599 non-null float646 total sulfur dioxide 1599 non-null float647 density 1599 non-null float648 pH 1599 non-null float649 sulphates 1599 non-null float6410 alcohol 1599 non-null float6411 quality 1599 non-null int64
'''
df.describe().T
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| fixed acidity | 1599.0 | 8.320 | 1.741 | 4.600 | 7.100 | 7.900 | 9.200 | 15.900 |
| volatile acidity | 1599.0 | 0.528 | 0.179 | 0.120 | 0.390 | 0.520 | 0.640 | 1.580 |
| citric acid | 1599.0 | 0.271 | 0.195 | 0.000 | 0.090 | 0.260 | 0.420 | 1.000 |
| residual sugar | 1599.0 | 2.539 | 1.410 | 0.900 | 1.900 | 2.200 | 2.600 | 15.500 |
| chlorides | 1599.0 | 0.087 | 0.047 | 0.012 | 0.070 | 0.079 | 0.090 | 0.611 |
| free sulfur dioxide | 1599.0 | 15.875 | 10.460 | 1.000 | 7.000 | 14.000 | 21.000 | 72.000 |
| total sulfur dioxide | 1599.0 | 46.468 | 32.895 | 6.000 | 22.000 | 38.000 | 62.000 | 289.000 |
| density | 1599.0 | 0.997 | 0.002 | 0.990 | 0.996 | 0.997 | 0.998 | 1.004 |
| pH | 1599.0 | 3.311 | 0.154 | 2.740 | 3.210 | 3.310 | 3.400 | 4.010 |
| sulphates | 1599.0 | 0.658 | 0.170 | 0.330 | 0.550 | 0.620 | 0.730 | 2.000 |
| alcohol | 1599.0 | 10.423 | 1.066 | 8.400 | 9.500 | 10.200 | 11.100 | 14.900 |
| quality | 1599.0 | 5.636 | 0.808 | 3.000 | 5.000 | 6.000 | 6.000 | 8.000 |
各个变量分布的直方图：
# 设置调色板
color = sns.color_palette()
column= df.columns.tolist()
fig = plt.figure(figsize = (10,8))
for i in range(12):plt.subplot(4,3,i+1)df[column[i]].hist(bins = 100,color = color[3])plt.xlabel(column[i],fontsize = 12)plt.ylabel('Frequency',fontsize = 12)
plt.tight_layout()
可以大致看出每个特征的分布情况，在
fig = plt.figure(figsize = (10,8))
for i in range(12):plt.subplot(4,3,i+1)sns.boxplot(df[column[i]],orient = 'v',width = 0.5,color = color[4])plt.ylabel(column[i],fontsize = 12)
plt.tight_layout()
酸性相关的特征分析
该数据集与酸度相关的特征有’fixed acidity’, ‘volatile acidity’, ‘citric acid’,‘chlorides’, ‘free sulfur dioxide’, ‘total sulfur dioxide’,‘PH’。其中前6中酸度特征都会对PH产生影响。PH在对数尺度，然后对6中酸度取对数做直方图。
acidityfeat = ['fixed acidity', 'volatile acidity', 'citric acid', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide',]fig = plt.figure(figsize = (10,6))
for i in range(6):plt.subplot(2,3,i+1)v = np.log10(np.clip(df[acidityfeat[i]].values,a_min = 0.001,a_max = None))plt.hist(v,bins = 50,color = color[0])plt.xlabel('log('+ acidityfeat[i] +')',fontsize = 12)plt.ylabel('Frequency')
plt.tight_layout()
plt.figure(figsize = (6,3))bins = 10**(np.linspace(-2,2))
plt.hist(df['fixed acidity'],bins = bins, edgecolor = 'k',label = 'fixed acidity')
plt.hist(df['volatile acidity'],bins = bins, edgecolor = 'k',label = 'volatile acidity')
plt.hist(df['citric acid'],bins = bins, alpha = 0.8,edgecolor = 'k',label = 'citric acid')plt.xscale('log')
plt.xlabel('Acid concentration(g/dm^3)')
plt.ylabel('Frequency')
plt.title('Historgram of Acid Concentration')
plt.legend()
plt.tight_layout()
df.describe().T
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| fixed acidity | 1599.0 | 8.320 | 1.741 | 4.600 | 7.100 | 7.900 | 9.200 | 15.900 |
| volatile acidity | 1599.0 | 0.528 | 0.179 | 0.120 | 0.390 | 0.520 | 0.640 | 1.580 |
| citric acid | 1599.0 | 0.271 | 0.195 | 0.000 | 0.090 | 0.260 | 0.420 | 1.000 |
| residual sugar | 1599.0 | 2.539 | 1.410 | 0.900 | 1.900 | 2.200 | 2.600 | 15.500 |
| chlorides | 1599.0 | 0.087 | 0.047 | 0.012 | 0.070 | 0.079 | 0.090 | 0.611 |
| free sulfur dioxide | 1599.0 | 15.875 | 10.460 | 1.000 | 7.000 | 14.000 | 21.000 | 72.000 |
| total sulfur dioxide | 1599.0 | 46.468 | 32.895 | 6.000 | 22.000 | 38.000 | 62.000 | 289.000 |
| density | 1599.0 | 0.997 | 0.002 | 0.990 | 0.996 | 0.997 | 0.998 | 1.004 |
| pH | 1599.0 | 3.311 | 0.154 | 2.740 | 3.210 | 3.310 | 3.400 | 4.010 |
| sulphates | 1599.0 | 0.658 | 0.170 | 0.330 | 0.550 | 0.620 | 0.730 | 2.000 |
| alcohol | 1599.0 | 10.423 | 1.066 | 8.400 | 9.500 | 10.200 | 11.100 | 14.900 |
| quality | 1599.0 | 5.636 | 0.808 | 3.000 | 5.000 | 6.000 | 6.000 | 8.000 |
甜度（sweetness）
residual sugar主要与酒的甜度有关，干红（<= 4g/L），半干（4-12g/L），半甜（12-45g/L），甜（>= 45g/L），该数据集中没有甜葡萄酒。
df['sweetness'] = pd.cut(df['residual sugar'],bins = [0,4,12,45],labels = ['dry','semi-dry','semi-sweet'])
df.head()
| fixed acidity | volatile acidity | citric acid | residual sugar | chlorides | free sulfur dioxide | total sulfur dioxide | density | pH | sulphates | alcohol | quality | sweetness | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7.4 | 0.70 | 0.00 | 1.9 | 0.076 | 11.0 | 34.0 | 0.998 | 3.51 | 0.56 | 9.4 | 5 | dry |
| 1 | 7.8 | 0.88 | 0.00 | 2.6 | 0.098 | 25.0 | 67.0 | 0.997 | 3.20 | 0.68 | 9.8 | 5 | dry |
| 2 | 7.8 | 0.76 | 0.04 | 2.3 | 0.092 | 15.0 | 54.0 | 0.997 | 3.26 | 0.65 | 9.8 | 5 | dry |
| 3 | 11.2 | 0.28 | 0.56 | 1.9 | 0.075 | 17.0 | 60.0 | 0.998 | 3.16 | 0.58 | 9.8 | 6 | dry |
| 4 | 7.4 | 0.70 | 0.00 | 1.9 | 0.076 | 11.0 | 34.0 | 0.998 | 3.51 | 0.56 | 9.4 | 5 | dry |
plt.figure(figsize = (6,4))
df['sweetness'].value_counts().plot(kind = 'bar',color = color[0])
plt.xticks(rotation = 0)
plt.xlabel('sweetness')
plt.ylabel('frequency')plt.tight_layout()
print('Figure 5')
# 创建一个新特征total acid
df['total acid'] = df['fixed acidity'] + df['volatile acidity'] + df['citric acid']columns = df.columns.tolist()
columns.remove('sweetness')
columns['fixed acidity','volatile acidity','citric acid','residual sugar','chlorides','free sulfur dioxide','total sulfur dioxide','density','pH','sulphates','alcohol','quality','total acid']
sns.set_style('ticks')
sns.set_context('notebook',font_scale = 1.1)column = columns[0:11] + ['total acid']
plt.figure(figsize = (10,8))
for i in range(12):plt.subplot(4,3,i+1)sns.boxplot(x = 'quality',y = column[i], data = df,color = color[1],width = 0.6)plt.ylabel(column[i],fontsize = 12)
plt.tight_layout()print('Figure 7:PhysicoChemico Propertise and Wine Quality by Boxplot')
从上图可以看出：
- 红酒品质与柠檬酸，硫酸盐，酒精度成正相关
- 红酒品质与易挥发性酸，密度，PH成负相关
- 残留糖分，氯离子，二氧化硫对红酒品质没有什么影响
sns.set_style('dark')
plt.figure(figsize = (10,8))
mcorr = df[column].corr()
mask = np.zeros_like(mcorr,dtype = np.bool)
mask[np.triu_indices_from(mask)] = True
cmap = sns.diverging_palette(220, 10, as_cmap=True)
g = sns.heatmap(mcorr, mask=mask, cmap=cmap, square=True, annot=True, fmt='0.2f')print('Figure 8:Pairwise colleration plot')
密度和酒精浓度
密度和酒精浓度是相关的，物理上，但两者并不是线性关系。另外密度还与酒精中的其中物质含量有关，但是相关性很小。
sns.set_style('ticks')
sns.set_context('notebook',font_scale = 1.4)plt.figure(figsize = (6,4))
sns.regplot(x = 'density',y = 'alcohol',data = df,scatter_kws = {'s':10},color = color[1])
plt.xlabel('density',fontsize = 12)
plt.ylabel('alcohol',fontsize = 12)plt.xlim(0.989,1.005)
plt.ylim(7,16)print('Figure 9: Density vs Alcohol')
酸性物质含量和PH
因为PH和非挥发性酸之间存在着-0.68的相关性，因为非挥发性酸的总量特别高，所以total acid这个指标意义不大。
column
['fixed acidity','volatile acidity','citric acid','residual sugar','chlorides','free sulfur dioxide','total sulfur dioxide','density','pH','sulphates','alcohol','total acid']
acidity_raleted = ['fixed acidity','volatile acidity','total sulfur dioxide','chlorides','total acid']plt.figure(figsize = (10,6))for i in range(5):plt.subplot(2,3,i+1)sns.regplot(x = 'pH',y = acidity_raleted[i],data = df,scatter_kws = {'s':10},color = color[1])plt.xlabel('PH',fontsize = 12)plt.ylabel(acidity_raleted[i],fontsize = 12)plt.tight_layout()
print('Figure 10:The correlation between different acid and PH')
多变量分析
与红酒品质相关性最高的三个特征分别是酒精浓度，挥发性酸含量，柠檬酸。下面研究三个特征对红酒的品质有何影响。
plt.style.use('ggplot')plt.figure(figsize = (6,4))
sns.lmplot(x = 'alcohol',y = 'volatile acidity',hue = 'quality',data = df,fit_reg = False,scatter_kws = {'s':10},size = 5)
print('Figure 11-1:scatter plot between alcohol and volatile acidity and quality')
sns.lmplot(x = 'alcohol', y = 'volatile acidity', col='quality', hue = 'quality', data = df,fit_reg = False, size = 3, aspect = 0.9, col_wrap=3,scatter_kws={'s':20})
print("Figure 11-2: Scatter Plots of Alcohol, Volatile Acid and Quality")
PH和非挥发性酸，柠檬酸
PH和非挥发性酸，柠檬酸成负相关。
sns.set_style('ticks')
sns.set_context("notebook", font_scale= 1.4)plt.figure(figsize=(6,5))
cm = plt.cm.get_cmap('RdBu')
sc = plt.scatter(df['fixed acidity'], df['citric acid'], c=df['pH'], vmin=2.6, vmax=4, s=15, cmap=cm)
bar = plt.colorbar(sc)
bar.set_label('pH', rotation = 0)
plt.xlabel('fixed acidity')
plt.ylabel('citric acid')
plt.xlim(4,18)
plt.ylim(0,1)
print('Figure 12: pH with Fixed Acidity and Citric Acid')
总结
对于红酒品质影响最重要的三个特征：酒精度、挥发性酸含量和柠檬酸。对于品质高于7的优质红酒和品质低于4的劣质红酒，直观上线性可分，对于品质为5和6的红酒很难进行线性区分。
数据建模
- 线性回归
- 集成算法
- 提升算法
- 模型评估
- 确定模型参数
1.数据集切分
1.1 切分特征和标签
1.2 切分训练集个测试集
df.head()
| fixed acidity | volatile acidity | citric acid | residual sugar | chlorides | free sulfur dioxide | total sulfur dioxide | density | pH | sulphates | alcohol | quality | sweetness | total acid | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7.4 | 0.70 | 0.00 | 1.9 | 0.076 | 11.0 | 34.0 | 0.998 | 3.51 | 0.56 | 9.4 | 5 | dry | 8.10 |
| 1 | 7.8 | 0.88 | 0.00 | 2.6 | 0.098 | 25.0 | 67.0 | 0.997 | 3.20 | 0.68 | 9.8 | 5 | dry | 8.68 |
| 2 | 7.8 | 0.76 | 0.04 | 2.3 | 0.092 | 15.0 | 54.0 | 0.997 | 3.26 | 0.65 | 9.8 | 5 | dry | 8.60 |
| 3 | 11.2 | 0.28 | 0.56 | 1.9 | 0.075 | 17.0 | 60.0 | 0.998 | 3.16 | 0.58 | 9.8 | 6 | dry | 12.04 |
| 4 | 7.4 | 0.70 | 0.00 | 1.9 | 0.076 | 11.0 | 34.0 | 0.998 | 3.51 | 0.56 | 9.4 | 5 | dry | 8.10 |
# 数据预处理工作# 检查数据的完整性
df.isnull().sum()
fixed acidity 0
volatile acidity 0
citric acid 0
residual sugar 0
chlorides 0
free sulfur dioxide 0
total sulfur dioxide 0
density 0
pH 0
sulphates 0
alcohol 0
quality 0
sweetness 0
total acid 0
dtype: int64
# 将object类型的数据转化为int类型
sweetness = pd.get_dummies(df['sweetness'])
df = pd.concat([df,sweetness],axis = 1)
df.head()
| fixed acidity | volatile acidity | citric acid | residual sugar | chlorides | free sulfur dioxide | total sulfur dioxide | density | pH | sulphates | alcohol | quality | sweetness | total acid | dry | semi-dry | semi-sweet | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7.4 | 0.70 | 0.00 | 1.9 | 0.076 | 11.0 | 34.0 | 0.998 | 3.51 | 0.56 | 9.4 | 5 | dry | 8.10 | 1 | 0 | 0 |
| 1 | 7.8 | 0.88 | 0.00 | 2.6 | 0.098 | 25.0 | 67.0 | 0.997 | 3.20 | 0.68 | 9.8 | 5 | dry | 8.68 | 1 | 0 | 0 |
| 2 | 7.8 | 0.76 | 0.04 | 2.3 | 0.092 | 15.0 | 54.0 | 0.997 | 3.26 | 0.65 | 9.8 | 5 | dry | 8.60 | 1 | 0 | 0 |
| 3 | 11.2 | 0.28 | 0.56 | 1.9 | 0.075 | 17.0 | 60.0 | 0.998 | 3.16 | 0.58 | 9.8 | 6 | dry | 12.04 | 1 | 0 | 0 |
| 4 | 7.4 | 0.70 | 0.00 | 1.9 | 0.076 | 11.0 | 34.0 | 0.998 | 3.51 | 0.56 | 9.4 | 5 | dry | 8.10 | 1 | 0 | 0 |
df = df.drop('sweetness',axis = 1)
labels = df['quality']
features = df.drop('quality',axis = 1)# 对原始数据集进行切分
from sklearn.model_selection import train_test_split
train_features,test_features,train_labels,test_labels = train_test_split(features,labels,test_size = 0.3,random_state = 0)print('训练特征的规模:',train_features.shape)
print('训练标签的规模:',train_labels.shape)
print('测试特征的规模:',test_features.shape)
print('测试标签的规模:',test_labels.shape)
训练特征的规模: (1119, 15)
训练标签的规模: (1119,)
测试特征的规模: (480, 15)
测试标签的规模: (480,)
from sklearn.linear_model import LinearRegression
LR = LinearRegression()
LR.fit(train_features,train_labels)prediction = LR.predict(test_features)
prediction[:5]
array([5.75571751, 4.82871294, 6.59036909, 5.36644662, 5.89993476])
#对模型进行评估
from sklearn.metrics import mean_squared_error
RMSE = np.sqrt(mean_squared_error(test_labels,prediction))
print('线性回归模型的预测误差:',RMSE)
线性回归模型的预测误差: 0.6332278109768246
# 对训练特征和测试特征做标准化处理，观察结果from sklearn.preprocessing import StandardScaler
train_features_std = StandardScaler().fit_transform(train_features)
test_features_std = StandardScaler().fit_transform(test_features)
LR = LinearRegression()
LR.fit(train_features_std,train_labels)
prediction = LR.predict(test_features_std)#观察预测结果误差
RMSE = np.sqrt(mean_squared_error(prediction,test_labels))
print('线性回归模型预测误差:',RMSE)
线性回归模型预测误差: 0.6351421172394885
对比原始数据与做了标准化处理的数据，其结果相差不大，所以该数据集不需要做标准化处理。
集成算法：随机森林
from sklearn.ensemble import RandomForestRegressor
RF = RandomForestRegressor()
RF.fit(train_features,train_labels)
prediction = RF.predict(test_features)
RMSE = np.sqrt(mean_squared_error(prediction,test_labels))
print('随机森林模型的预测误差:',RMSE)
随机森林模型的预测误差: 0.6142407237123461
RF.get_params
<bound method BaseEstimator.get_params of RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,max_features='auto', max_leaf_nodes=None,min_impurity_decrease=0.0, min_impurity_split=None,min_samples_leaf=1, min_samples_split=2,min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,oob_score=False, random_state=None, verbose=0, warm_start=False)>
from sklearn.model_selection import GridSearchCV
param_grid = {'n_estimators':[100,200,300,400,500],'max_depth':[3,4,5,6],'min_samples_split':[2,3,4]}RF = RandomForestRegressor()
grid = GridSearchCV(RF,param_grid = param_grid,scoring = 'neg_mean_squared_error',cv = 3,n_jobs = -1)
grid.fit(train_features,train_labels)
GridSearchCV(cv=3, error_score='raise-deprecating',estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,max_features='auto', max_leaf_nodes=None,min_impurity_decrease=0.0, min_impurity_split=None,min_samples_leaf=1, min_samples_split=2,min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,oob_score=False, random_state=None, verbose=0, warm_start=False),fit_params=None, iid='warn', n_jobs=-1,param_grid={'n_estimators': [100, 200, 300, 400, 500], 'max_depth': [3, 4, 5, 6], 'min_samples_split': [2, 3, 4]},pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',scoring='neg_mean_squared_error', verbose=0)
grid.best_params_{'max_depth': 6, 'min_samples_split': 2, 'n_estimators': 300}
RF = RandomForestRegressor(n_estimators = 300,min_samples_split = 2,max_depth = 6)RF.fit(train_features,train_labels)
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=6,max_features='auto', max_leaf_nodes=None,min_impurity_decrease=0.0, min_impurity_split=None,min_samples_leaf=1, min_samples_split=2,min_weight_fraction_leaf=0.0, n_estimators=300, n_jobs=None,oob_score=False, random_state=None, verbose=0, warm_start=False)
prediction = RF.predict(test_features)RF_RMSE = np.sqrt(mean_squared_error(prediction,test_labels))
print('随机森林模型的预测误差:',RF_RMSE)
随机森林模型的预测误差: 0.6153424077044428
集成算法：GBDT
from sklearn.ensemble import GradientBoostingRegressorGBDT = GradientBoostingRegressor()
GBDT.fit(train_features,train_labels)
gbdt_prediction = GBDT.predict(test_features)
gbdt_RMSE = np.sqrt(mean_squared_error(gbdt_prediction,test_labels))print('GBDT模型的预测误差:',gbdt_RMSE)
GBDT模型的预测误差: 0.6232190669430115
GBDT.get_params
<bound method BaseEstimator.get_params of GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,learning_rate=0.1, loss='ls', max_depth=3, max_features=None,max_leaf_nodes=None, min_impurity_decrease=0.0,min_impurity_split=None, min_samples_leaf=1,min_samples_split=2, min_weight_fraction_leaf=0.0,n_estimators=100, n_iter_no_change=None, presort='auto',random_state=None, subsample=1.0, tol=0.0001,validation_fraction=0.1, verbose=0, warm_start=False)>
随机参数搜索模型 RandomizedSearchCV
from sklearn.model_selection import RandomizedSearchCV
GBDT = GradientBoostingRegressor()
#设置GBDT算法的部分参数
learning_rate = [0.01,0.1,1,10]
max_depth = [3,4,5,6]
min_samples_leaf = [1,2,4]
min_samples_split = [2,5,10]
n_estimators = [int(x) for x in range(100,600,100)]random_params_group = {'learning_rate':learning_rate,'max_depth':max_depth,'min_samples_leaf':min_samples_leaf,'min_samples_split':min_samples_split,'n_estimators':n_estimators}random_model = RandomizedSearchCV(GBDT,param_distributions = random_params_group,n_iter = 100,scoring = 'neg_mean_squared_error',verbose = 2,n_jobs = -1,cv = 3,random_state = 0)
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