I am very new to Machine Learning, and this is my first project on which I am working. I am trying a binary classification problem, trying to predict claim(yes/no) from an insurance dataset. In my dataset, I have about 7% positive class (1534 Positive datapoints, 21076 Negative datapoints). I have 7 features (2 numerical, 5 categorical).
My model is giving the following performance metrics:
precision recall f1-score support 0 0.95 0.70 0.81 6311 1 0.12 0.52 0.19 472 avg / total 0.89 0.69 0.76 6783
Being a beginner, I am having difficulty understanding how good/bad my model is based on these metrics. How much can I improve my model, given the small and highly skewed dataset which I have? I have taken the following approach: Randomly sample 50-50 splits of the data 10 times, train a Logistic Regression on each, and then average the predictions of all the 10 models.