PyCaret is a high-level, low-code Python library that makes it easy to compare, train, evaluate, tune, and deploy machine learning models with only a few lines of code . At its core, PyCaret is basically just a large wrapper over many data science libraries such as Scikit-learn, Yellowbrick, SHAP, Optuna, and Spacy.
Read moreIs sklearn used in production?
The variety of machine learning techniques in combination with the solid implementations that scikit-learn offers makes it a one-stop-shopping library for machine learning in Python . Moreover, its consistent API, well-tested code and permissive licensing allow us to use it in a production environment.
Read moreHow does PyCaret work?
PyCaret is an open-source, low-code machine learning library in Python that aims to reduce the cycle time from hypothesis to insights.
Read moreWhat is gluon TS?
Gluon Time Series (GluonTS) is the Gluon toolkit for probabilistic time series modeling, focusing on deep learning-based models .
Read moreWhat is Horizon in time series forecasting?
The forecast horizon is the length of time into the future for which forecasts are to be prepared . These generally vary from short-term forecasting horizons (less than three months) to long-term horizons (more than two years).
Read moreWhat is time series forecasting methods?
Time series forecasting occurs when you make scientific predictions based on historical time stamped data . It involves building models through historical analysis and using them to make observations and drive future strategic decision-making.
Read moreWhat is one step ahead forecasting?
One-step ahead forecasts are computed sequentially for each data point by using computed level and trend states for the current point, and seasonal states for the last seasonal period . Forecast error is computed by subtracting forecast value at the previous point from the observed value at the current point.
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