liblinear | large data for which with/without nonlinear mappings | Machine Learning library

I built a Logistic Regression Model to predict Loan Acceptors. The dataset is 94% non acceptors and 6% acceptors. I've run several logistic regression models one with the original dataset, one after upsampling to 50/50 and removing some predictor variables, and one without the upsampling, but after removing some predictor variables.

Model 1: Better than 90% accuracy, precision and recall on 25 feature columns. After running the model, I output the predicting to a different CSV (same people as original csv though) and it's returning 10,000 acceptors. My guess was this could be caused by overfitting? Wasn't sure, but then tried it on the same 94% non-acceptors and 6% acceptors, but with fewer variables (19 feature columns). This time the accuracy is 81%, but the precision is only 21%, while recall is 765 (for training and test). This time it only returns 8 total acceptors ( out of 18,000)

Finally, I tried upsampling and upsampled to a balanced set. The accuracy is only 68% (which I can work with) and the precision and recall is 66% for both training and test. Ran the model then outputted the prediction to the csv file (again same people, different CSV file, not sure if that's messing it up) and this time it returned 0 acceptors.

Does anyone have any advice on what is causing this and how I can fix this?

I'm not sure which regression code would be most beneficial. I'm happy to post the upsampling code if that would be more helpful.

The goal of this program is to predict the number of stars from a number of features in a github repo.

This works fine, good accuracy, now what I want, is to find the feature importance of these features. I see the coef_ being used a lot for this as it seems a simple solution.

The issue I am having is that coef_ returns a lis of lists 3x10 (10 being the number of features as seen below)

My question is, why are 3 different lists returned?

I am trying to scale my data within the crossvalidation folds of a MLENs Superlearner pipeline. When I use StandardScaler in the pipeline (as demonstrated below), I receive the following warning:

/miniconda3/envs/r_env/lib/python3.7/site-packages/mlens/parallel/_base_functions.py:226: MetricWarning: [pipeline-1.mlpclassifier.0.2] Could not score pipeline-1.mlpclassifier. Details: ValueError("Classification metrics can't handle a mix of binary and continuous-multioutput targets") (name, inst_name, exc), MetricWarning)

Of note, when I omit the StandardScaler() the warning disappears, but the data is not scaled.

I am trying to run GridSearchCV with the LogisticRegression estimator and record the model accuracy, precision, recall, f1 metrics.

However, I get the following error on the precision metric:

I tried to train a LinearSVC model and evaluate it with cross_val_score on a linearly separable dataset that I created, but I'm getting an error.

Here is a reproducible example:

I am trying to solve a multiclass classification problem using Logistic regression. My dataset has 3 distinct classes, and each data point belongs to only one class. Here is the sample training_data;

Here the first column is vector of ones I have added as bias term. And the target column has been binarized using the concept of label binarize, as mentioned in sickit-learn

Then I got the target as follows;

I'm currently getting a mixture of the following errors:

• ValueError: Unknown label type: 'unknown'
• ValueError: Expected 2D array, got 1D array instead: array=[0. 0. 0. ... 1. 1. 1.]. Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
• TypeError: 'tuple' object is not callable

When I search for others who have had the same issue, the answer usually leads me from one of the above errors to another. Below is a screenshot of my code. Lines 7-9 are the solutions I found for my errors that just lead to different errors. Comment out line 8 or 9 or both and it gives you the wrong shape error. Comment out all three and you get the label type unknown error.

For line 7 I have tried bool, int, and float.

I want to train linear SVM with multilabel classification with the following code:

I got a convergence warning using linear support vector machine in Scikit learn with breast cancer data.

Below is the code: