Is it possible for a ML model to perform worse than random classification? I implemented different machine learning algorithms on a matrix with binary data to predict a univariate target with two classes. 


random forest (accuracy = 62.01)
Neural Network(acc= 58.9)
svm-radial kernel (accuracy = 58.02)
linear discriminant analysis(accuracy = 57.9)
logistic regression(accuracy = 57.6). 


My baseline accuracy is 52.55. But in case of Naive Bayes in same setting gives only 48.5 accuracy that identifies only one class in y. predict. 

Is it possible for a machine learning model to behave worse than a random classification?
Yes, it's possible.

Just means the model is adjusting to noise, so it's valuing the "wrong" features. 

As an analogy, if you were randomly guessing basketball game outcomes, you'd probably perform better than someone who thinks less points is better and is guessing based on previous games' scores