I have little experience in the field of Machine Learning, so I apologise in advance for my ignorance and lack of intuitions.
I'm trying to build a classifying algorithm to identify if a subject is healthy or ill. I have the following data:
- 17 subjects.
- Each subject has 1140 features, ranging from -1 to 1.
- Each subject has a label from 0 to 6. 0 meaning very ill and 6 completely healthy.
- I only have data of subjects with labels from 0 to 2.
- The features (X) have low correlation with the labels (Y).
The way I obtained the features is irrelevant to this question, so I'm not going to go through it.
This is how the data looks:
Each colour represents a subject. As I have mentioned, I only have data of subjects with labels from 0 to 2, that is, Y=0, Y=1 or Y=2. And the features (X) are then 17 Subjects x 1140 features.
What I want to do is the following:
- Fit the data into a Logistic Regression.
- Use the Recursive Feature Elimination algorithm in order to fit the data into the classification function and know how many features I need to select so that its accuracy is high.
- Use Stratified Cross Validation to enhance the accuracy.
This is how I've implemented the algorithm in Python.
skf = StratifiedShuffleSplit(n_splits=50, test_size=0.3) # Cross-validation 50 times estimator = LogisticRegression(C=1) # The estimator used is Logistic Regression selector = RFECV(estimator, step=1, cv=skf, scoring="accuracy") # Run RFE selector = selector.fit(X, Y) # Fit the data print(selector.grid_scores_) # Print accuracy
And I obtained an accuracy around 60% for all the features, as shown below:
So, my question is. Do you think I implemented the code in the right way? Do you have any suggestion to enhance the accuracy? Maybe adjust a bit the parameters?
Thanks a lot. I hope I made myself clear.