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Data nature:

I have features with 10 numeric type, and other 10 categorical, with a lot of values, at the end, using one-hot encoding I got a matrix of 600 columns. My problem is with accuracy which is 0.7, knowing that other peers got more that 0.9.

Problem:

Target data is binary, and is not evenly distributed at all. Trying blindly after pre-processing from sklearn.linear_model import LogisticRegression and sklearn.svm scored using roc_auc_score: .7 and .75.

Back to basics, I run this

train['cible'].value_counts() / train['cible'].count()

and got

1    0.970791
0    0.029209
Name: cible, dtype: float64

Quite interesting I think, but how can I improve accuracy. Any hints ?

Note: I will edit and add False Positive Rate and True Positive Rate as I lost output, after scaling, missing data imputation and retraining the model which takes couple of hours.

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From scikitlearn LogisticRegression docs:

class_weight : dict or ‘balanced’, default: None

Weights associated with classes in the form {class_label: weight}. If not given, all classes are supposed to have weight one. The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)). Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified. New in version 0.17: class_weight=’balanced’

So try to add class_weight='balanced'in your call to LogisticRegression()

Or maybe if this doesn't work, try to use as trainSet an evenly split dataset: where the number of samples of class 1 is equal to class 0.

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  • $\begingroup$ I will give it a try and be back $\endgroup$ – bacloud14 Sep 27 '18 at 16:48
  • $\begingroup$ From 0.75 to 0.8 ! No guess this helped in my case. $\endgroup$ – bacloud14 Sep 28 '18 at 16:52

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