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Microsoft

Survival Analysis API built with Azure Machine Learning

published by: Microsoft

Survival Analysis API is an example built with Microsoft Azure Machine Learning that computes the probability when an event occurs. Under many scenarios, the main outcome under assessment is the time to an event of interest. In other words, the question “when will an event occur” is asked. Some examples include situations where the data describes the elapsed time (days, years, mileage, etc.) until the event of interest (disease relapse, PhD degree received, brake pad failed) occurs. Each instance in the data represents a specific object (a patient, a person, a car, etc.). This web service helps address the question “what is the probability that the event of interest will occur by time n for object x?” By providing a survival analysis model, this web service enables the users to supply data to train the model and test it. The main theme of the experiment is to model the length of the elapsed time until the event of interest occurs.

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Microsoft

Binomial Distribution Generator API built with Azure Machine Learning

published by: Microsoft

The Binomial Distribution Generator API is an example built with Microsoft Azure Machine Learning that helps generate and handle binomial distributions. This service is a part of the Binomial distribution suite of services that allows the user to generate a binomial distribution sequence of any length (this specific service), calculate quantiles out of given probability and calculate probability from a given quantile. Each of the services emit different outputs based on the selected service (see description below). The Binomial Distribution Suite is based on R functions qbinom, rbinom and pbinom that are included in R stats package. More information can be found here.

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Microsoft

Binomial Distribution Probability Calculator API built with Azure Machine Learning

published by: Microsoft

The Binomial Distribution Probability Calculator API is an example built with Microsoft Azure Machine Learning that helps generate and handle binomial distributions. This service is a part of the Binomial distribution suite of services that allows the user to generate a binomial distribution sequence of any length, calculate quantiles out of given probability and calculate probability from a given quantile (this specific service). Each of the services emit different outputs based on the selected service (see description below). The Binomial Distribution Suite is based on R functions qbinom, rbinom and pbinom that are included in R stats package.

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Microsoft

Forecasting - AutoRegressive Integrated Moving Average (ARIMA) API built with Azure Machine Learning

published by: Microsoft

The Forecasting - AutoRegressive Integrated Moving Average (ARIMA) API is an example built with Microsoft Azure Machine Learning that fits an ARIMA model to user inputted data and outputs predicted forecasted value for future dates. Will the demand for a specific product increase this year? Can I predict my product sales for the Christmas season, so that I can effectively plan my inventory? Forecasting models are apt to address such questions. Given the past data, these models examine hidden trends and seasonality to predict future trends. This web service implements Autoregressive Integrated Moving Average (ARIMA) to produce predictions based on the historical data provided by the user.

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Microsoft

Normal Distribution Generator API built with Azure Machine Learning

published by: Microsoft

The Normal Distribution Generator API is an example built with Microsoft Azure Machine Learning that helps generate and handle normal distributions. This service is a part of the Normal distribution suite of services that allows the user to generate a normal distribution sequence of any length (this specific service), calculate quantiles out of given probability and calculate probability from a given quantile. Each of the services emit different outputs based on the selected service. The Normal Distribution Suite is based on R functions qnorm, rnorm and pnorm that are included in R stats package.

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Microsoft

Difference in Proportions Test API built with Azure Machine Learning

published by: Microsoft

Difference in Proportions Test API is an example built with Microsoft Azure Machine Learning that conducts a hypothesis test based on user inputted data and outputs the test results. Are two proportions statistically different? Suppose a user wants to compare two movies to determine if one movie has a significantly higher proportion of ‘likes’ when compared to the other. This web service conducts a hypothesis test of the difference in two proportions based on user input of number of successes and total number of trials for the 2 comparison groups. A scenario would be where this web service could be called from within a movie comparison app, telling the user based on movie ratings whether one of the movies is really ‘liked’ more often that the other.

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Microsoft

Forecasting - ETS + STL API built with Azure Machine Learning

published by: Microsoft

Forecasting - ETS + STL API is an example built with Microsoft Azure Machine Learning that fits a ETS + STL model to user inputted data and outputs the forecasting value for observations in the data. Will the demand for a specific product increase this year? Can I predict my product sales for the Christmas season, so that I can effectively plan my inventory? Forecasting models are apt to address such questions. Given the past data, these models examine hidden trends and seasonality to predict future trends. This web service implements Seasonal Trend decomposition (STL) and Exponential Smoothing model (ETS) to produce predictions based on the historical data provided by the user.

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Microsoft

Normal Distribution Quantile Calculator API built with Azure Machine Learning

published by: Microsoft

The Normal Distribution Quantile Calculator API is an example built with Microsoft Azure Machine Learning that helps generate and handle normal distributions. This service is a part of the Normal distribution suite of services that allows the user to generate a normal distribution sequence of any length, calculate quantiles out of given probability (this specific service) and calculate probability from a given quantile. Each of the services emit different outputs based on the selected service. The Normal Distribution Suite is based on R functions qnorm, rnorm and pnorm that are included in R stats package.

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Microsoft

Forecasting - Exponential Smoothing (ETS) API built with Azure Machine Learning

published by: Microsoft

Forecasting - Exponential Smoothing (ETS) API is an example built with Microsoft Azure Machine Learning that fits an Exponential Smoothing model to user inputted data and outputs the forecasting value for observations in the data. Will the demand for a specific product increase this year? Can I predict my product sales for the Christmas season, so that I can effectively plan my inventory? Forecasting models are apt to address such questions. Given the past data, these models examine hidden trends to predict future trends. This web service implements Exponential Smoothing model (ETS) to produce predictions based on the historical data provided by the user.

Data
Microsoft

Normal Distribution Probability Calculator API built with Azure Machine Learning

published by: Microsoft

The Normal Distribution Probability Calculator API is an example built with Microsoft Azure Machine Learning that helps generate and handling normal distributions. This service is a part of the Normal distribution suite of services that allows the user to generate a normal distribution sequence of any length, calculate quantiles out of given probability and calculate probability from a given quantile (this specific service). Each of the services emit different outputs based on the selected service. The Normal Distribution Suite is based on R functions qnorm, rnorm and pnorm that are included in R stats package.

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