I know this will be very basic, however I'm really confused and I would like to understand parameter tuning better.
I'm working on a benchmark dataset that is already partitioned to three splits training, development, and testing and I would like to tune my classifier parameters using GridSearchCV from sklearn.
What is the correct partition to tune the parameter? is it the development or the training?
I've seen researchers in the literature mentioning that they "tuned the parameters using GridSearchCV on the development split" another example is found here;
Do they mean they trained on the training split then tested on the development split? or do ML practitioners usually mean they perform the GridSearchCV entirely on the development split?
I'd really appreciate a clarification. Thanks,
Usually in a 3-way split you train a model using a training set, then you validate it on a development (which is also called validation set) set to tune hyperpameters and then after all the tuning is complete you perform a final evaluation of a model on an unseen before testing set (also known as evaluation set).
In a two-way split you just have a train set and a test set, so you perform tuning/evaluation on the same test set.
Related
Our team is currently using CatBoost to develop credit scoring models, and our current process is to...
Sort the data chronologically for out-of-time sampling, and split it into train, valid, and test sets
Perform feature engineering
Perform feature selection and hyperparameter tuning (mainly learning rate) on train, using valid as an eval set for early stopping
Perform hyperparameter tuning on the combination of train and valid, using test as an eval set for early stopping
Evaluate the results of Step #4 using standard metics (RMSE, ROC AUC, etc.)
However, I am concerned that we may be overfitting to the test set in Step #4.
In Step #4, should we just be refitting the model on train and valid without tuning (i.e., using the selected features and hyperparameters from Step #3)?
The motivation for having a Step #4 at all is to train the models on more recent data due to our out-of-time sampling scheme.
Step #4 falls outside of the best practices for machine learning.
When you create the test set, you need to set it aside and only use it at the end to evaluate how successful your model(s) are at making predictions. Do not use the test set to inform hyperparameter tuning! If you do, you will overfit your data.
Try using cross-validation instead:
Dataset contains :
15000 Observations/Rows
3000 Features/Columns
Can I train Machine Learning model on these Dataset
Yes, you can apply the ML model but before that understanding of your problem statement come into a picture with all of the feature name available in the data set. If you are having big dataset try to convert it into a cluster of 2 or else take a small dataset to analyze what your data speaks about.
That is why population & sampling come to practical use.
You have to check whether accuracy of the train data set & test data set should be the same, if not then your model is memorizing instead of learning & here Regularization in Machine Learning comes into a picture.
No one can answer this based on the information you provided. The simplest approach is to run a sanity check in the form of cross validation. Does your model perform well on unseen data? If it does, it is probably not overfit. If it does not, check if the model is performing well on the training data. A model that performs well on training data but not on unseen data is the definition of a model being overfit.
I am trying to perform cross-validation on NMF to find the best parameters to use. I tried using the sklearn cross-validation but get an error that states the NMF does not have a scoring method. Could anyone here help me with that? Thank you all
A property of nmf is that it is an unsupervised (machine learning) method. This generally means that there is no labeled data that can serve as a 'golden standard'.
In case of NMF you can not define what is the 'desired' outcome beforehand.
The cross validation in sklearn is designed for supervised machine learning, in which you have labeled data by definition.
What cross validation does, it holds out sets of labeled data, then trains a model on the data that is leftover and evaluates this model on the held out set. For this evaluation any metric can be used. For example: accuracy, precision, recall and F-measure, and for computing these measures it needs labeled data.
I'm wondering if the result of the test set is used to make the optimization of model's weights. I'm trying to make a model but the issue I have is I don't have many data because they are medical research patients. The number of patient is limited in my case (61) and I have 5 feature vectors per patient. What I tried is to create a deep learning model by excluding one subject and I used the exclude subject as the test set. My problem is there is a large variability in subject features and my model fits well the training set (60 subjects) but not that good the 1 excluded subject.
So I'm wondering if the testset (in my case the excluded subject) could be used in a certain way to make converge the model to better classify the exclude subject?
You should not use the test data of your data set in your training process. If your training data is not enough, one approach using a lot during this days(especially for medical images) is data augmentation. So I highly recommend you to use this technique in your training process. How to use Deep Learning when you have Limited Data is one of the good tutorial about data augmentation.
No , you souldn't use your test set for training to prevent overfitting , if you use cross-validation principles you need exactly to split your data into three datasets a train set which you'll use to train your model , a validation set to test different value of your hyperparameters , and a test set to finally test your model , if you use all your data for training, your model will overfit obviously.
One thing to remember deep learning work well if you have a large and very rich datasets
What does these codes mean?
can you explain to me :
features_train, labels_train, features_test, labels_test = makeTerrainData()
def submitAccuracy():
return acc
In machine learning development you want to split your available data into train/test sets and if possible an additional validation set. You do this to test for overfitting and ensure your model is generalizable to unseen observations. The final validation set is often useful because without knowing it, often users will try to optimize their parameters on the test partition accuracy, and in doing so are basically giving hints to the model of what that data is. The validation set is useful to test that this hasn't occurred and your model isn't overfit.
With only seeing the code provided, train_features likely corresponds to the actual data being used to develop the model, in the train partition. The labels are the categories you are trying to predict.
The test partition is simply a random sample of your available data. Features/labels are the same as above.
You want to build the model off of the training data, and assess accuracy on the test partition.
Sebastian Rascka provides a marvelous overview of machine learning in python. The code samples and some explanations can be found at https://github.com/rasbt/python-machine-learning-book/tree/master/code