  This thesis designs a prediction system based on matrix factorization to
predict the classification accuracy of a specific model on a particular
dataset. In this thesis, we conduct comprehensive empirical research on more
than fifty datasets that we collected from the openml website. We study the
performance prediction of three fundamental machine learning algorithms,
namely, random forest, XGBoost, and MultiLayer Perceptron(MLP). In particular,
we obtain the following results: 1. Predictability of fine-tuned models using
coarse-tuned variants. 2. Predictability of MLP using feature extraction
techniques. 3. Predict model performance using implicit feedback.
