[20180310]_决策树

Directory: Mathimatics-Numerical algorithms
Plat: Python
Size: 2812KB
Downloads: 1
Upload time: 2019-10-09 14:30:30
Uploader: 花好月圆夜
Description:   The combination of decision tree algorithm in machine learning and artificial intelligence learning is the only way to machine learning and artificial intelligence learning and the best learning material for beginners.

File list:
[20180310]_决策树, 0 , 2019-10-09
[20180310]_决策树\.ipynb_checkpoints, 0 , 2019-10-09
[20180310]_决策树\.ipynb_checkpoints\00_KNN案例:鸢尾花数据分类-checkpoint.ipynb, 41283 , 2018-03-04
[20180310]_决策树\.ipynb_checkpoints\01_决策树案例一:鸢尾花数据分类-checkpoint.ipynb, 117968 , 2018-03-10
[20180310]_决策树\.ipynb_checkpoints\02_决策树案例一:鸢尾花数据特征属性比较-checkpoint.ipynb, 160037 , 2018-01-08
[20180310]_决策树\.ipynb_checkpoints\03_决策树案例二:波士顿房屋租赁价格预测(自己写一下)-checkpoint.ipynb, 443869 , 2018-03-01
[20180310]_决策树\.ipynb_checkpoints\04_决策树过拟合和欠拟合-checkpoint.ipynb, 53471 , 2018-01-08
[20180310]_决策树\.ipynb_checkpoints\05_决策树回归模型可视化-checkpoint.ipynb, 506672 , 2018-03-10
[20180310]_决策树\.ipynb_checkpoints\06_决策树分类模型可视化-checkpoint.ipynb, 152394 , 2018-03-10
[20180310]_决策树\.ipynb_checkpoints\08_综合案例:分类算法比较-checkpoint.ipynb, 1135702 , 2018-01-08
[20180310]_决策树\0.png, 79564 , 2018-03-10
[20180310]_决策树\00_KNN案例:鸢尾花数据分类.ipynb, 41283 , 2018-03-04
[20180310]_决策树\01_决策树案例一:鸢尾花数据分类.ipynb, 120868 , 2018-03-19
[20180310]_决策树\02_决策树案例一:鸢尾花数据特征属性比较.ipynb, 160036 , 2018-03-01
[20180310]_决策树\03_决策树案例二:波士顿房屋租赁价格预测(自己写一下).ipynb, 150489 , 2018-03-01
[20180310]_决策树\04_决策树过拟合和欠拟合.ipynb, 55958 , 2018-03-01
[20180310]_决策树\05_决策树回归模型可视化.ipynb, 506672 , 2018-03-10
[20180310]_决策树\06_决策树分类模型可视化.ipynb, 152394 , 2018-03-10
[20180310]_决策树\datas, 0 , 2019-10-09
[20180310]_决策树\datas\boston_housing.data, 49081 , 2018-01-08
[20180310]_决策树\datas\iris.data, 4551 , 2018-01-08
[20180310]_决策树\datas\newdata.csv, 180556 , 2018-01-08
[20180310]_决策树\datas\risk_factors_cervical_cancer.csv, 102059 , 2018-01-08
[20180310]_决策树\datas\yinzi.csv, 591 , 2018-01-08
[20180310]_决策树\iris.dot, 1859 , 2018-03-10

Download users:

Relate files:

Comment: Add Comment

Favorite users: