Logistic Regression outperforms MLPClassifier I am new in ML and I am trying to train classifier. I have a tiny dataset, just 90 examples, I divided it 70/30 train/test set and started to train.

As I know MLP must outperform Logistic Regression, but I have an opposite situation in my case, firstly I thought that I give bad hyperparameters, so I tried to use GridSearchCV for finding best hyperparameters, but after it, again Logistic Regression was better, I am wondering maybe is it because of the small number of dataset or I missed something?
In ML, all algorithms are useful depending on the dataset. Its naive to generalize an algorithm to be always better than the other.

In your case since you have only 90 examples, mlpclassifier couldn’t have trained proper as compared to logreg.

A general suggestion: if you post like rows of your dataset along with accuracy of the two algorithms, it would have been a bit easier to discuss why there were differences.