CMPS 460 | Machine Learning | S22 | Session 6.a | Learning from Imbalanced Data

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  1. Tamer Elsayed
Рекомендации по теме
CMPS 460 | 0:44:50

CMPS 460 | Machine Learning | S22 | Session 9.e | Neural Networks (Training I)

CMPS 460 | 0:43:37

CMPS 460 | Machine Learning | S22 | Session 9.a | Neural Networks (Architecture)

CMPS 460 | 0:41:30

CMPS 460 | Machine Learning | S22 | Session 4.d | Perceptron (IV)

CMPS 460 | 0:46:48

CMPS 460 | Machine Learning | S22 | Session 4.c | Perceptron (III)

CMPS 460 | 0:41:40

CMPS 460 | Machine Learning | S22 | Session 9.b | Neural Networks (Representational Power)

CMPS 460 | 0:35:25

CMPS 460 | Machine Learning | S22 | Session 6.a | Learning from Imbalanced Data

CMPS 460 | 0:45:19

CMPS 460 | Machine Learning | S22 | Session 6.b | Multi-class Classification

CMPS 460 | 0:45:43

CMPS 460 | Machine Learning | S22 | Session 9.g | Neural Networks (Training III)

CMPS 460 | 0:44:16

CMPS 460 | Machine Learning | S22 | Session 8.e | Probabilistic Modeling (Logistic Regression)

CMPS 460 | 0:40:55

CMPS 460 | Machine Learning | S22 | Session 8.a | Probabilistic Modeling (Probability Review)

CMPS 460 | 0:41:50

CMPS 460 | Machine Learning | S22 | Session 1.c | Decision Trees (I)

CMPS 460 | 0:49:33

CMPS 460 | Machine Learning | S22 | Session 9.c | Neural Networks (Backpropagation I)

CMPS 460 | 0:39:59

CMPS 460 | Machine Learning | S22 | Session 5.a | Practical Issues (Dealing with Features I)

CMPS 460 | 0:36:43

CMPS 460 | Machine Learning | S22 | Session 3.c | K-means Clustering (I)

CMPS 460 | 0:31:12

CMPS 460 | Machine Learning | S22 | Session 8.c | Probabilistic Modeling (Naive Bayes II)

CMPS 460 | 0:39:03

CMPS 460 | Machine Learning | S22 | Session 7.b | Linear Models (Regularization)

CMPS 460 | 0:42:28

CMPS 460 | Machine Learning | S22 | Session 4.a | Perceptron (I)

CMPS 460 | 0:42:33

CMPS 460 | Machine Learning | S22 | Session 3.a | kNN (I)

CMPS 460 | 0:41:48

CMPS 460 | Machine Learning | S22 | Session 5.c | Practical Issues (Evaluation I)

CMPS 460 | 0:45:44

CMPS 460 | Machine Learning | S22 | Session 9.f | Neural Networks (Training II)

CMPS 460 | 0:39:14

CMPS 460 | Machine Learning | S22 | Session 7.c | Linear Models (Gradient Descent I)

CMPS 460 | 0:50:33

CMPS 460 | Machine Learning | S22 | Session 1.a | Welcome to ML

CMPS 460 | 0:54:16

CMPS 460 | Machine Learning | S22 | Session 8.b | Probabilistic Modeling (Naive Bayes I)

CMPS 460 | 0:47:30

CMPS 460 | Machine Learning | S22 | Session 3.b | kNN (II)

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33:01 I think in knn(the simplest version of it) as a multiclass classification because if we have 2(class 1), 2(class 2) and 1(class3) with a given K=5 so we cannot decide wheter the point belongs to class 1 or class 2. I think we must use some techniques like One-versus-all or One-versus-one to make it suitable to be used with multiclass classification.

-Mohamed_bayan