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Bagging:Code Sample

Instructor: Applied AI Course Duration: 6 mins

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Bagging :Train and Run-time Complexity.

Extremely randomized trees

Decision Trees

1.1

Geometric Intuition: Axis parallel hyperplanes

17 min

1.2

Sample Decision tree

8 min

1.3

Building a decision Tree:Entropy

19 min

1.4

Building a decision Tree:Information Gain

10 min

1.5

Building a decision Tree: Gini Impurity

7 min

1.6

Building a decision Tree: Constructing a DT

21 min

1.7

Building a decision Tree: Splitting numerical features

8 min

1.8

Feature standardization

4 min

1.9

Building a decision Tree:Categorical features with many possible values

7 min

1.10

Overfitting and Underfitting

8 min

1.11

Train and Run time complexity

7 min

1.12

Regression using Decision Trees

9 min

1.13

12 min

1.14

9 min

1.15

Exercise: Decision Trees on Amazon reviews dataset

3 min

Ensemble Models

2.1

What are ensembles?

6 min

2.2

Bootstrapped Aggregation (Bagging) Intuition

17 min

2.3

Random Forest and their construction

15 min

2.4

Bias-Variance tradeoff

7 min

2.5

Bagging :Train and Run-time Complexity.

9 min

2.6

Bagging:Code Sample

6 min

2.7

Extremely randomized trees

8 min

2.8

Random Tree :Cases

6 min

2.9

Boosting Intuition

17 min

2.10

Residuals, Loss functions and gradients

13 min

2.11

Gradient Boosting

10 min

2.12

Regularization by Shrinkage

8 min

2.13

Train and Run time complexity

6 min

2.14

XGBoost: Boosting + Randomization

14 min

2.15

AdaBoost: geometric intuition

7 min

2.16

Stacking models

9 min

2.17

Cascading classifiers

15 min

2.18

Kaggle competitions vs Real world

9 min

2.19

Exercise: Apply GBDT and RF to Amazon reviews dataset.

4 min