Fritz vs Xcessiv

Fully define your data source, cross-validation process, relevant metrics, and base learners with Python code;Any model following the Scikit-learn API can be used as a base learner;Task queue based architecture lets you take full advantage of multiple cores and embarrassingly parallel hyperparameter searches;Direct integration with TPOT for automated pipeline construction;Automated hyperparameter search through Bayesian optimization;Easy management and comparison of hundreds of different model-hyperparameter combinations;Automatic saving of generated secondary meta-features;Stacked ensemble creation in a few clicks;Automated ensemble construction through greedy forward model selection;Export your stacked ensemble as a standalone Python file to support multiple levels of stacking

Image labeling - With the Image Labeling feature, you can identify the contents of an image or each frame of live video. Each prediction returns a set of labels as well as a confidence score for each label. Image Labeling can recognize people, places, and things. The underlying ML model was trained on millions of images and hundreds of labels.; Object detection - With the Object Detection feature, you can identify objects of interest in an image or each frame of live video. Each prediction returns a set of objects, each with a label, bounding box, and confidence score.