08:45

How are Variable Interactions Modeled in Decision Trees (CART)?

02:13

Bootstrap Sampling

11:27

How to Write a Decision Tree (CART) as an Equation

58:04

3 Ways to Improve Your Regression, Part 2

54:04

3 Ways to Improve Your Regression, Part 1

57:56

Forecasting with Predictive Analytics

59:37

Tips & Tricks for Segmentation (Targeting, Profiling, Classification)

57:13

Tips & Tricks for Improving Your Logistic Regression

54:14

Maximizing ROI (Using State-of-the-Art Data Science Techniques)

53:37

3 Ways to Improve Your Regression

47:25

Enter a KDD Cup or Kaggle Competition. You Don't Need to Be an Expert!

19:16

Data Mining For Statisticians Part 6

06:37

Data Mining For Statisticians Part 5

07:12

Data Mining for Statisticians Part 4

15:40

Data Mining for Statisticians Part 3

18:34

Data Mining For Statisticians Part 2

18:20

Data Mining For Statisticians Part 1

08:51

Battery: Train Test Part 2

14:11

Battery: Train Test Part 1

09:48

Modeling Automation: Target Part 2, Model-based missing value imputation

13:55

Modeling Automation: Target Part 1, Testing for multivariate relationships among predictors)

07:39

Battery: Shaving Part 3

07:28

Battery: Shaving Part 2

11:52

Battery: Priors Part 1

13:59

Battery: One Off

14:07

Data Mining Automation with Batteries: Introduction

11:16

Battery: Shaving Part 1

08:52

Battery: Priors Part 2

04:34

Battery: Priors Part 3

14:52

Save, Score, Translate Predictive Models For Future Deployment

11:49

Building a TreeNet Gradient Boosting Model

09:45

Building a CART Model

06:39

Opening and Working with a Data File

11:24

Part 2: Least Squares Deviation Cost For Regression

19:34

Part 8: Direct Interpretation Of Response Using Logistic Function

11:48

Part 1: An Introduction To Understanding Cost Functions

13:47

Part 3: Least Absolute Deviation And Huber M Cost

09:39

Part 4: Introduction To Binary Classification

13:18

Part 5: Evaluating Prediction Success With Precision And Recall

19:33

Part 6: Measuring Performance With The ROC Curve

21:12

Part 7: Measuring Model Performance With Gains And Lift

23:30

Part 9: Multinomial Classification- Expected Cost

19:25

Part 10: Multinomial Classification- Log Likelihood

14:53

Build A Classification Model In Random Forests

22:15

Build A Classification Tree In CART

09:06

Building A Regression Model In TreeNet

14:11

Building A Classification Model In TreeNet

14:32

Building A Regression Model In MARS

25:03

Data Mining: How to Work with Different Cost Functions

00:55

Data Mining in Higher Education

32:20

Time Series-Style Predictive Modeling in SPM Using LAGS

16:50

Build a Random Forests Model Using a Gradient Boosting Algorithm

49:13

Building Random Forests Models with Treenet and LAGS for Time Series Modeling

01:07

Easy Data Mining Get Started

52:19

Evolution of Regression Modeling Part 4

39:48

Data Mining Tutorial: Using Battery PRIORS in SPM

00:34

Getting Started With Data Mining

13:42

Data Mining Tutorial: Train-Test Consistency In CART

32:03

Data Mining Tutorial: Introduction To Cross Validation

18:04

Data Mining Tutorial: Repeated Cross Validation In SPM