Home Courses Netflix Movie recommendation system Bagging :Train and Run-time Complexity.

Bagging :Train and Run-time Complexity.

Instructor: Applied AI Course Duration: 9 mins

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Bias-Variance tradeoff

Bagging:Code Sample

Ensemble Models

1.1

What are ensembles?

6 min

1.2

Bootstrapped Aggregation (Bagging) Intuition

17 min

1.3

Random Forest and their construction

15 min

1.4

Bias-Variance tradeoff

7 min

1.5

Bagging :Train and Run-time Complexity.

9 min

1.6

Bagging:Code Sample

4 min

1.7

Extremely randomized trees

8 min

1.8

Random Tree :Cases

6 min

1.9

Boosting Intuition

17 min

1.10

Residuals, Loss functions and gradients

13 min

1.11

Gradient Boosting

10 min

1.12

Regularization by Shrinkage

8 min

1.13

Train and Run time complexity

6 min

1.14

XGBoost: Boosting + Randomization

14 min

1.15

AdaBoost: geometric intuition

7 min

1.16

Stacking models

22 min

1.17

Cascading classifiers

15 min

1.18

Kaggle competitions vs Real world

9 min

1.19

Exercise: Apply GBDT and RF to Amazon reviews dataset.

4 min