Sounds good, Sebastian.. thanks for the suggestions.. My dataset is relatively small (only ~35 samples), and this is the workflow I have set up so far.. 1. Model selection: use nested loop using cross_val_score(GridSearchCV(...),...) same as shown in the scikit-learn page that you provided - the results show no statistically significant difference in accuracy mean +/- SD among classifiers.. this is expected as the pattern is pretty obvious and simple to separate by eyes after dimensionality reduction (I use pipeline of stdscaler, LDA, and classifier)... so i take all of them and use voting classifier in step #3.. 2. Hyperparameter optimization: use GridSearchCV to optimize hyperparameters of each classifiers 3. Decision Region: use the hyperparameters from step #2, fit each classifier separately to the whole dataset, and use voting classifier to get decision region
This sounds reasonable? Thank you very much! Raga On Thu, Jan 26, 2017 at 8:31 PM, Sebastian Raschka <[email protected]> wrote: > You are welcome! And in addition, if you select among different > algorithms, here are some more suggestions > > a) don’t do it based on your independent test set if this is going to your > final model performance estimate, or be aware that it would be overly > optimistic > b) also, it’s not the best idea to select algorithms using > cross-validation on the same training set that you used for model > selection; a more robust way would be nested CV (e.g,. > http://scikit-learn.org/stable/auto_examples/model_ > selection/plot_nested_cross_validation_iris.html) > > But yeah, it all depends on your dataset and size. If you have a neural > net that takes week to train, and if you have a large dataset anyway so > that you can set aside large sets for testing, I’d train on > train/validation splits and evaluate on the test set. And to compare e.g., > two networks against each other on large test sets, you could do a McNemar > test. > > Best, > Sebastian > > > On Jan 26, 2017, at 8:09 PM, Raga Markely <[email protected]> > wrote: > > > > Ahh.. nice.. I will use that.. thanks a lot, Sebastian! > > > > Best, > > Raga > > > > On Thu, Jan 26, 2017 at 6:34 PM, Sebastian Raschka <[email protected]> > wrote: > > Hi, Raga, > > > > I think that if GridSearchCV is used for classification, the stratified > k-fold doesn’t do shuffling by default. > > > > Say you do 20 grid search repetitions, you could then do sth like: > > > > > > from sklearn.model_selection import StratifiedKFold > > > > for i in range(n_reps): > > k_fold = StratifiedKFold(n_splits=5, shuffle=True, random_state=i) > > gs = GridSearchCV(..., cv=k_fold) > > ... > > > > Best, > > Sebastian > > > > > On Jan 26, 2017, at 5:39 PM, Raga Markely <[email protected]> > wrote: > > > > > > Hello, > > > > > > I was trying to do repeated Grid Search CV (20 repeats). I thought > that each time I call GridSearchCV, the training and test sets separated in > different splits would be different. > > > > > > However, I got the same best_params_ and best_scores_ for all 20 > repeats. It looks like the training and test sets are separated in > identical folds in each run? Just to clarify, e.g. I have the following > data: 0,1,2,3,4. Class 1 = [0,1,2] and Class 2 = [3,4]. Suppose I call cv = > 2. The split is always for instance [0,3] [1,2,4] in each repeat, and I > couldn't get [1,3] [0,2,4] or other combinations. > > > > > > If I understand correctly, GridSearchCV uses StratifiedKFold when I > enter cv = integer. The StratifiedKFold command has random state; I wonder > if there is anyway I can make the the training and test sets randomly > separated each time I call the GridSearchCV? > > > > > > Just a note, I used the following classifiers: Logistic Regression, > KNN, SVC, Kernel SVC, Random Forest, and had the same observation > regardless of the classifiers. > > > > > > Thank you very much! > > > Raga > > > > > > _______________________________________________ > > > scikit-learn mailing list > > > [email protected] > > > https://mail.python.org/mailman/listinfo/scikit-learn > > > > _______________________________________________ > > scikit-learn mailing list > > [email protected] > > https://mail.python.org/mailman/listinfo/scikit-learn > > > > _______________________________________________ > > scikit-learn mailing list > > [email protected] > > https://mail.python.org/mailman/listinfo/scikit-learn > > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn >
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