The Microsoft Time Series algorithm uses a combination of ARIMA analysis and linear regression based on decision trees to analyze time-related data, such as monthly sales data or yearly profits. The patterns it discovers can be used to predict values for future time steps. The algorithm can be customized to use either the decision tree method, ARIMA, or both.
The Microsoft Logistic Regression algorithm is a regression algorithm that works well for regression modeling. This algorithm ...
The Microsoft Naive Bayes algorithm is a classification algorithm that is quick to build, and works well for predictive modeling. ...
The Microsoft Neural Network algorithm uses a gradient method to optimize parameters of multilayer networks to predict multiple ...
The Microsoft Sequence Clustering algorithm is a combination of sequence analysis and clustering, which identifies clusters ...
The Microsoft Time Series algorithm uses a combination of ARIMA analysis and linear regression based on decision trees to ...
The MINIMUM_ITEMSET_SIZE parameter for the '%{modelname/}' model is not valid. MINIMUM_ITEMSET_SIZE must be between 1 and ...
The MINIMUM_PROBABILITY parameter for the '%{modelname/}' model is not valid. MINIMUM_PROBABILITY must be between 0 and 1. ...
The MINIMUM_SUPPORT parameter for the '%{modelname/}' model is not valid. MINIMUM_SUPPORT must be greater than or equal to ...
The MINIMUM_SUPPORT parameter is negative in the '%{modelname/}' Microsoft Sequence Clustering model. MINIMUM_SUPPORT must ...