# Increase accuracy in binary classification with ambiguous data

I am fairly new to Datascience and currently working on an assignment that requires me to do a binary classification on a set with about 9 parameters for X. I tried working on it using different methods implemented in scikit (linear regression, neural network, random forests,..). However, my ROC-Curve always ended up looking barely better than random. After looking closer at the scatterplot for all nine parameters against Y, I realized that the datapoints at Y=1 and Y=0 overlapped greatly for all of them, making them somehow 'ambiguous' (see picture). Is there any way I can increase the accuracy of my model by pre-processing the data? Or any other means? I can not change the classification of test and training set.

• What are you plotting in the figure? Please, export figures with axis labels and title... It seems to me that you are plotting the labels... Feb 5 at 15:29

• Scatter plot is not usually performed to visualize $$Y \text{ vs } feature_i$$. If you do so, and your points dont overlap, your classification algorithm will be (you can exchange the values for $$Y$$): $$Y = 1 \text{ if } feature_i >= \alpha \text{ otherwise } Y = 0$$. i.e. you can classify your dataset with a threshold over a certain single feature.