Impossible to increase model accuracy I'm building binary classification models on my company's dataset. The problem I'm having is that I haven't been able to increase the accuracy of my models. I have trained, tuned, cross validated models such as logistic regression, knn, neural networks and they all have results with the same accuracy. I feel that I have tried everything. The dataset is about 100 data points with 21 features. I'm aware of the curse of dimensionality so I have tried using only subsets of the features but the accuracy is the same no matter which subsets of features I use.
Is it possible that with this dataset, no further improvements can be done? Should I just tell my boss that this is the maximum accuracy possible given the amount of data we have?
100 data points is a very very low number of points. I am quite surprised that the algo managed to learn something meaningful (well unless you have very obvious relationships between the features and the target). I'll suggest you look at what the logistic regression learned exactly (what coefficients are deemed significant). There is an important risk that you are overfitting on your very very small dataset. And you can't really catch that so your are just left with checking if the rationale of your model make sense.
Yes, you should probably tell your boss that this is not enough data points.