Disturbances in the near-Earth space weather environment caused by solar activities can have severe impacts on many facilities on which we have become increasingly dependent. Forecasting space weather by predicting sources of space weather disturbances, such as solar wind, coronal mass ejections, solar flares, and solar energetic particles, has become a very active area of research. Different tools, ranging from simple empirical models to highly complex physics-based numerical models, are being developed for forecasting. One of the most important aspects of a forecast is its performance. This session discusses various ways to quantify the performance scores and uncertainties in different space weather forecast models for solar and heliospheric conditions. Contributions regarding all aspects of space weather forecast model development are welcome. Example topics include using observations to constrain models, improving the physics and accuracy of numerical models, applying machine learning techniques for forecasting, and designing prediction performance scores.