232
votes
Accepted
Train/Test/Validation Set Splitting in Sklearn
You could just use sklearn.model_selection.train_test_split twice. First to split to train, test and then split train again into validation and train. Something ...
- 2,672
79
votes
Train/Test/Validation Set Splitting in Sklearn
There is a great answer to this question over on SO that uses numpy and pandas.
The command (see the answer for the discussion):
...
- 935
59
votes
Accepted
Merging multiple data frames row-wise in PySpark
Stolen from: https://stackoverflow.com/questions/33743978/spark-union-of-multiple-rdds
Outside of chaining unions this is the only way to do it for DataFrames.
...
- 9,348
58
votes
Accepted
What is the difference between bootstrapping and cross-validation?
Both cross validation and bootstrapping are resampling methods.
bootstrap resamples with replacement (and usually produces new "surrogate" data sets with the same number of cases as the original ...
47
votes
Accepted
Why use both validation set and test set?
Let's assume that you are training a model whose performance depends on a set of hyperparameters. In the case of a neural network, these parameters may be for instance the learning rate or the number ...
- 1,787
45
votes
Accepted
How to use the output of GridSearch?
Decided to go away and find the answers that would satisfy my question, and write them up here for anyone else wondering.
The .best_estimator_ attribute is an instance of the specified model type, ...
- 1,732
41
votes
Train/Test/Validation Set Splitting in Sklearn
Adding to @hh32's answer, while respecting any predefined proportions such as (75, 15, 10):
...
- 511
37
votes
Accepted
How does the validation_split parameter of Keras' fit function work?
You actually would not want to resample your validation set after each epoch. If you did this your model would be trained on every single sample in your dataset and thus this will cause overfitting. ...
- 8,776
26
votes
Accepted
Cross validation Vs. Train Validate Test
If k-fold cross-validation is used to optimize the model parameters, the training set is split into k parts. Training happens k times, each time leaving out a different part of the training set. ...
- 502
24
votes
Accepted
How to calculate the fold number (k-fold) in cross validation?
The number of folds is usually determined by the number of instances contained in your dataset. For example, if you have 10 instances in your data, 10-fold cross-validation wouldn't make sense. $k$-...
- 8,776
19
votes
Accepted
Cross Validation in Keras
From the Keras documentation, you can load the data into Train and Test sets like this:
(X_train, y_train), (X_test, y_test) = mnist.load_data()
As for cross ...
- 446
18
votes
Accepted
When do I have to use aucPR instead of auROC? (and vice versa)
Yes, you are correct that the dominant difference between the area under the curve of a receiver operator characteristic curve (ROC-AUC) and the area under the curve of a Precision-Recall curve (PR-...
- 6,798
18
votes
Why use both validation set and test set?
The test set and cross validation set have different purposes. If you drop either one, you lose its benefits:
The cross validation set is used to help detect over-fitting and to assist in hyper-...
- 28.6k
17
votes
Merging multiple data frames row-wise in PySpark
Sometime, when the dataframes to combine do not have the same order of columns, it is better to df2.select(df1.columns) in order to ensure both df have the same ...
- 271
16
votes
Accepted
How to choose a classifier after cross-validation?
You do cross-validation when you want to do any of these two things:
Model Selection
Error Estimation of a Model
Model selection can come in different scenarios:
Selecting one algorithm vs others ...
- 1,490
13
votes
How to calculate the fold number (k-fold) in cross validation?
Depends on how much CPU juice you are willing to afford for the same. Having a lower K means less variance and thus, more bias, while having a higher K means more variance and thus, and lower bias.
...
- 8,256
13
votes
Accepted
Cross validation for highly imbalanced data with undersampling
You should always do your evaluation of model performance on data that has not been over/undersampled. You can setup a pipeline with scikit-learn to perform your undersampling on the training set and ...
- 682
12
votes
Accepted
What is GridSearchCV doing after it finishes evaluating the performance of parameter combinations that takes so long?
Yep I figured it out. The answer is that by default GridSearchCV's last act is to expose the API of the estimator object you passed so that you can directly call things like ...
- 1,744
12
votes
Accepted
Why you shouldn't upsample before cross validation
To see clearly why the procedure of upsampling before CV is mistaken and it leads to data leakage and other undesired consequences, it is useful to imagine first the simpler "baseline" case, ...
- 1,948
11
votes
Accepted
How to estimate GridSearchCV computing time?
You could fit your model/pipeline (with default parameters) to your data once and see how long it takes to train. Then you would multiply that by how many times you want to train the model through ...
- 7,908
11
votes
Why is the k-fold cross-validation needed?
Given the randomization, it is unlikely that there will be a dramatic change from one run into the next one in the loop of the cross-validation.
This assumption is wrong, it's true only if the ...
- 25k
10
votes
Train/Test/Validation Set Splitting in Sklearn
You can use train_test_split twice. I think this is most straightforward.
...
- 201
10
votes
Accepted
Can overfitting occur even with validation loss still dropping?
I am not sure if the validation set is balanced or not. You have a severe data imbalance problem. If you sample equally and randomly from each class to train your network, and then a percentage of ...
- 1,969
10
votes
Accepted
Validation vs. test vs. training accuracy. Which one should I compare for claiming overfit?
Which two accuracies I compare to see if the model is overfitting or not?
You should compare the training and test accuracies to identify over-fitting. A training accuracy that is subjectively far ...
- 9,227
10
votes
Why GridSearchCV returns nan?
By default, GridSearchCV provides a score of nan when fitting the model fails. You can change that behavior and raise an error ...
- 11.2k
9
votes
Accepted
Nested cross-validation and selecting the best regression model - is this the right SKLearn process?
Yours is not an example of nested cross-validation.
Nested cross-validation is useful to figure out whether, say, a random forest or a SVM is better suited for your problem. Nested CV only outputs a ...
- 3,410
9
votes
Accepted
Which is first ? Tuning the parameters or selecting the model
You can tune parameters only if you have already trained the model, otherwise there is nothing to tune.
However, i've also read that model selection shoud be done before tuning the parameters.
...
- 558
9
votes
Accepted
Using Cross Validation technique for a CNN model
Question 1: Why do most CNN models not apply the cross-validation technique?
$k$-fold cross-validation is often used for simple models with few parameters, models with simple hyperparameters and ...
- 1,918
8
votes
How to choose a classifier after cross-validation?
No. You don't select any of the k classifiers built during k-fold cross-validation. First of all, the purpose of cross-validation is not to come up with a predictive model, but to evaluate how ...
- 1,173
8
votes
Accepted
How to implement Python's MLPClassifier with gridsearchCV?
A tuple of the form $(i_1, i_2, i_3, ... , i_n)$ gives you a network with $n$ hidden layers, where $i_k$ gives you the number of neurons in the $k$th hidden layer.
If you want three hidden layers ...
oW_♦
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