User jennifer ruurs

7 votes

4 answers

2k views

Is k-fold cross-validation more effective than splitting the dataset into training and test datasets to prevent overfitting?

asked Nov 17, 2019 at 10:31

5 votes

1 answer

88 views

How could decision tree learning algorithms cope with imbalanced classes?

asked Nov 18, 2019 at 15:37

5 votes

1 answer

550 views

How can I avoid overfitting when doing parameter tuning?

asked Oct 23, 2019 at 14:40

5 votes

1 answer

858 views

Are deep learning models more prone to overfitting than machine learning ones?

asked Nov 8, 2019 at 14:48

5 votes

1 answer

106 views

Why would the application of boosting prevent underfitting?

asked Nov 14, 2019 at 8:09

4 votes

2 answers

81 views

Oposite type of predictions for unbalanced dataset

asked Nov 25, 2019 at 21:17

3 votes

1 answer

728 views

Why do Bayesian algorithms work well with small datasets?

asked Nov 23, 2019 at 8:10

3 votes

1 answer

197 views

What needs to be done to make a fair algorithm?

asked Nov 14, 2019 at 9:25

2 votes

1 answer

29 views

Can we say: the more we increase the numbers of cross validation the less likely it is that we overfit?

asked Oct 23, 2019 at 15:26

2 votes

1 answer

145 views

How can I determine the bias and variance of a random forrest?

asked Oct 19, 2019 at 8:18

2 votes

1 answer

1k views

How does the decision tree implicitly do feature selection?

asked Nov 23, 2019 at 11:26

2 votes

2 answers

866 views

When should I use a linear activation instead of ReLU?

asked Oct 21, 2019 at 21:11

1 vote

0 answers

30 views

How would the "best function" been constructed if there are no computationally limitations?

asked Nov 21, 2019 at 16:42

1 vote

1 answer

103 views

How should I interpret this validation plot?

asked Oct 18, 2019 at 14:22

0 votes

1 answer

98 views

How to interpret this learning curve plot

asked Oct 19, 2019 at 7:17

0 votes

1 answer

86 views

What can I do with an autoencoder? [duplicate]

asked Oct 20, 2019 at 5:12

Stack Exchange Network

16 Questions

Is k-fold cross-validation more effective than splitting the dataset into training and test datasets to prevent overfitting?

How could decision tree learning algorithms cope with imbalanced classes?

How can I avoid overfitting when doing parameter tuning?

Are deep learning models more prone to overfitting than machine learning ones?

Why would the application of boosting prevent underfitting?

Oposite type of predictions for unbalanced dataset

Why do Bayesian algorithms work well with small datasets?

What needs to be done to make a fair algorithm?

Can we say: the more we increase the numbers of cross validation the less likely it is that we overfit?

How can I determine the bias and variance of a random forrest?

How does the decision tree implicitly do feature selection?

When should I use a linear activation instead of ReLU?

How would the "best function" been constructed if there are no computationally limitations?

How should I interpret this validation plot?

How to interpret this learning curve plot

What can I do with an autoencoder? [duplicate]