Is there any way to plot the learning curves of training and validation data, when validation data does already exist as a holdout set?
sklearn learning_curve() does not offer such a paramater, as far as I can tell.
You can set the parameter cv as a PredefinedSplit (or just a length-1 iterator of train/test indices). See the description of cv in the documentation.
Related
I have some doubts about the implementation and tuning of parameters and hyperparameters by using the classic train, validation and test set. So it would be of great help if somebody could clarify me these concepts and bring me some hints for its implementation in a language like Python.
For example, if I have a Neural Network, for what I know the parameter tuning (lets consider the number of hidden layers and neurons per layer), could be tuned with the training set. So when it comes to the validation set, which is approximately 20% of the dataset, I can tune my hyperparameters with the following algorithm:
Example: Tuning batch size and learning rate:
hyperListB=[]
hyperListL=[]
//let´s suppose both lists have the same dimensions
for i in range(0,hyperListB):
model=fit(train_set,hyperListB[i],hyperlistL[i]
values[].add(evaluate(model,validation_set) //add scores of each run
end for
for i in range(0,values):
plot_loss_functions(values)
select best set of hyperparameters
model=fit(test_set, selecter_hyperparameters)
evaluate(model)
would this sequence of steps be correct? I have searched thru different pages and did not find something that could help me with this. Please, bear in mind that I do not want to use cross-validation or other library-based techniques such as GridSearchCV.
Thanks
In a Train validation test split, the fit method on the train data.
Validation data is used for hyperparameter tuning. A set of hyperparameters is selected and the model is trained on the train set. Then this model will be evaluated on the validation set. This is repeated until all permutations of the different hyperparameters have been exhausted.
The best set of hyperparameters are the ones that gave the best result on the validation set. This method is called Grid search.
The test set is used to evaluate the model with the best hyperparameters selected. THis gives the final unbiased accuracy and loss.
The fit method will never be called on the validation or test set.
your example will look like:
hyperListB=[]
hyperListL=[]
//let´s suppose both lists have the same dimensions
for hyperB in hyperListB:
for hyperL in hyperListL:
model=fit(train_set,hyperB,hyperL)
values[].add(evaluate(model,validation_set) //add scores of each run
end for
end for
for i in range(0,values):
plot_loss_functions(values)
select best set of hyperparameters
evaluate(model,test_set)
I have read the general step for K-fold cross validation under
https://machinelearningmastery.com/k-fold-cross-validation/
It describe the general procedure is as follows:
Shuffle the dataset randomly.
Split the dataset into k groups (folds)
For each unique group:Take the group as a hold out or test data set
Take the remaining groups as a training data set Fit a model on the training set and evaluate it on the test set
Retain the evaluation score and discard the model
Summarize the skill of the model using the sample of model
evaluation scores
So if it is K-fold then K models will be built, right? But why I read from the following link from H2O which is saying it built K+1 models?
https://github.com/h2oai/h2o-3/blob/master/h2o-docs/src/product/tutorials/gbm/gbmTuning.ipynb
Arguably, "I read somewhere else" is too vague a statement (where?), because context does matter.
Most probably, such statements refer to some libraries which, by default, after finishing the CV proper procedure, go on to build a model on the whole training data using the hyperparameters found by CV to give best performance; see for example the relevant train function of the caret R package, which, apart from performing CV (if requested), returns also the finalModel:
finalModel
A fit object using the best parameters
Similarly, scikit-learn GridSearchCV has also a relevant parameter refit:
refit : boolean, or string, default=True
Refit an estimator using the best found parameters on the whole dataset.
[...]
The refitted estimator is made available at the best_estimator_ attribute and permits using predict directly on this GridSearchCV instance.
But even then, the models fitted are almost never just K+1: when you use CV in practice for hyperparameter tuning (and keep in mind that there there are other uses, too, for CV), you will end up fitting m*K models, where m is the length of your hyperparameters combination set (all K-folds in a single round are run with one single set of hyperparameters).
In other words, if your hypeparameter search grid consists of, say, 3 values for the no. of trees and 2 values for the tree depth, you will fit 2*3*K = 6*K models during the CV procedure, and possibly +1 for fitting your model at the end to the whole data with the best hyperparameters found.
So, to summarize:
By definition, each K-fold CV procedure consists of fitting just K models, one for each fold, with fixed hyperparameters across all folds
In case of CV for hyperparameter search, this procedure will be repeated for each hyperparameter combination of the search grid, leading to m*K fits
Having found the best hyperparameters, you may want to use them for fitting the final model, i.e. 1 more fit
leading to a total of m*K + 1 model fits.
Hope this helps...
I am completely new to Machine Learning algorithms and I have a quick question with respect to Classification of a dataset.
Currently there is a training data that consists of two columns Message and Identifier.
Message - Typical message extracted from Log containing timestamp and some text
Identifier - Should classify the category based on the message content.
The training data was prepared by extracting a particular category from the tool and labelling it accordingly.
Now the test data contains just the message and I am trying to obtain the Category accordingly.
Which approach is most helpful in this scenario ? Is it the Supervised or Unsupervised Learning ?
I have a trained dataset and I am trying to predict the Category for the Test Data.
Thanks in advance,
Adam
If your labels are exact then you can classify using ANN, SVM etc. But labels are not exact you have to cluster data with respect to the features you have in data. K-means or nearest neighbour can be the starting point for clustering.
It is supervised learning, and a classification problem.
However, obviously you do not have the label column (the to-be-predicted value) for your testset. Thus, you cannot calculate error measures (such as False Positive Rate, Accuracy etc) for that test set.
You could, however, split the set of labeled training data that you do have into a smaller training set and a validation set. Split it 70%/30%, perhaps. Then build a prediction model from your smaller 70% training dataset. Then tune it on your 30% validation set. When accuracy is good enough, then apply it on your testset to obtain/predict the missing values.
Which techniques / algorithms to use is a different question. You do not give enough information to answer that. And even if you did you still need to tune the model yourself.
You have labels to predict, and training data.
So by definition it is a supervised problem.
Try any classifier for text, such as NB, kNN, SVM, ANN, RF, ...
It's hard to predict which will work best on your data. You willhave to try and evaluate several.
I am trying to plot a learning curve to figure out whether my model is suffering from high bias, and to achieve this I would need to plot the training set errors versus the cross validation set errors. In Scikit Learn is there a way to get this information?
rscv_rfc = grid_search.RandomizedSearchCV(RandomForestClassifier(), param_grid, n_jobs=4, cv=10)
rscv_rfc gives me the best estimator etc., alongwith the best params for the model. Is there a way to receive the mean cv error from this object?
The docstring of RandomizedSearchCV tells us that it exposes grid_scores_ containing all the scores it evaluated. However, these are all scores evaluated on held out data from splits of the training set.
Here is the place where the scores are actually evaluated. While the function _fit_and_score actually has an option return_train_scores, which you could set if you constructed your own grid search object, it is set to False here and thus the training scores remain unaccessible.
I am wondering whether it would be useful in general to have this option propagate through into the *SearchCV objects or not.
I'm using weka, I have a training set, and the classify of the examples in the training set is boolean.
After I have the training set, I want to predict the percentage of new input to be true or false. I want to get a number between 0-1, and not only o or 1.
How can I do that, I have seen that in the prediection there are only the possibels classifes.
Thanks in advance.
You can only make the same kind of prediction with the learned classifier -- it learns to make the predictions you train it to make. The kind of prediction you want sounds more like regression. That is, you're don't want a strict classification, but a continuous value designating the membership probability.
The easiest way to achieve what you want is to replace the Booleans in your training set with 0/1 values and learn a regression model. This will give you numbers, although not necessarily only between 0 and 1.
To get real probabilities, you would need to use a classifier that calculates probabilities (such as Naive Bayes) and write some custom code (using the Weka library) to retrieve them. See the javadoc of the method that gives you access to the class probabilities.