线性回归模型：线性回归对于特征的要求；处理长尾分布；理解线性回归模型；
模型性能验证：评价函数与目标函数；交叉验证方法；留一验证方法；针对时间序列问题的验证；绘制学习率曲线；绘制验证曲线；
嵌入式特征选择：Lasso回归；Ridge回归；决策树；
模型对比：常用线性模型；常用非线性模型；
模型调参：贪心调参方法；网格调参方法；贝叶斯调参方法
import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings('ignore')
#读取上次处理好的数据文件
sample_feature = reduce_mem_usage(pd.read_csv('data_for_tree.csv'))
#线性回归 & 五折交叉验证 & 模拟真实业务情况
sample_feature = sample_feature.dropna().replace('-', 0).reset_index(drop=True)
sample_feature['notRepairedDamage'] = sample_feature['notRepairedDamage'].astype(np.float32)
train = sample_feature[continuous_feature_names + ['price']]
train_X = train[continuous_feature_names]
train_y = train['price']
简单建模
from sklearn.linear_model
model = LineaRegression(normalize=True)
model = model.fit(train_X, train_y)
'intercept:'+ str(model.intercept_)
#查看训练的线性回归模型的截距（intercept）与权重(coef)
sorted(dict(zip(continuous_feature_names, model.coef_)).items(), key=lambda x:x[1], reverse=True)
from matplotlib import pyplot as plt
subsample_index = np.random.randint(low=0, high=len(train_y), size=50)
#绘制特征v_9的值与标签的散点图，图片发现模型的预测结果（蓝色点）与真实标签（黑色点）的分布差异较大，且部分预测值出现了小于0的情况，说明我们的模型存在一些问题
model = model.fit(train_X, train_y_ln)
print('intercept:'+ str(model.intercept_))
sorted(dict(zip(continuous_feature_names, model.coef_)).items(), key=lambda x:x[1], reverse=True)
plt.scatter(train_X['v_9'][subsample_index], train_y[subsample_index], color='black')
plt.scatter(train_X['v_9'][subsample_index], np.exp(model.predict(train_X.loc[subsample_index])), color='blue')
plt.xlabel('v_9')
plt.ylabel('price')
plt.legend(['True Price','Predicted Price'],loc='upper right')
print('The predicted price seems normal after np.log transforming')
plt.show()
线性回归模型 https://zhuanlan.zhihu.com/p/49480391
决策树模型 https://zhuanlan.zhihu.com/p/65304798
GBDT模型 https://zhuanlan.zhihu.com/p/45145899
XGBoost模型 https://zhuanlan.zhihu.com/p/86816771
LightGBM模型 https://zhuanlan.zhihu.com/p/89360721
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