Decision Trees Random Forests and Gradient Boosting in R
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3:23:35
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01-welcome to the course.mp4
02:53
02-section introduction.mp4
00:47
03-introduction to decision trees.mp4
06:14
04-building decision trees a.mp4
06:17
05-building decision trees b.mp4
07:10
06-building decision trees c.mp4
07:47
07-building decision trees d.mp4
06:55
08-section introduction.mp4
00:39
09-edutravel case.mp4
01:53
10-describing the dataset.mp4
06:18
11-importing csv data into r.mp4
04:16
12-changing the data type.mp4
05:17
13-dealing with missing data1.mp4
10:13
14-combining rare categories.mp4
03:53
15-data split training and testing datasets.mp4
07:49
16-section introduction.mp4
00:51
17-decision trees with ctree.mp4
06:57
18-interpretation of results.mp4
03:25
19-prediction with ctree.mp4
07:36
20-confusion matrix.mp4
07:03
21-roc curve.mp4
08:21
22-auc .mp4
05:42
23-section introduction.mp4
00:44
24-decisions trees with rpart.mp4
08:02
25-choosing complexity parameter.mp4
05:04
26-classification and confusion matrix.mp4
06:47
27-roc and auc rpart.mp4
06:13
28-section introduction.mp4
00:48
29-introduction to random forests.mp4
07:27
30-building a random forest in r.mp4
09:56
31-classification and confusion matrix.mp4
06:47
32-roc and auc (rf).mp4
06:15
33-section introduction.mp4
00:43
34-theoretical introduction to gradient boost.mp4
05:26
35-xgboost model.mp4
09:42
36-prediction and confusion matrix.mp4
04:04
37-roc and auc.mp4
06:25
38-conclusion.mp4
00:56
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- language english
- Training sessions 38
- duration 3:23:35
- Release Date 2024/03/06
