This article briefly describes the Machine Learning problems that the AI & Analytic Engine tackles.
The AI & Analytics Engine currently supports three types of Machine Learning problems, namely classification, regression, and time-series forecasting.
Classification (Supervised Learning)
Given an observation, predict which category something belongs to, from a set of categories.
Examples:
- Binary classification - If an email is spam or not? (yes/no)
- Multi-class classification - Which flower species does the observation belong to? (Iris setosa, Iris virginica, or Iris versicolor)
Regression (Supervised Learning)
Given an observation, predict the continuous variable.
Examples:
- Predict house prices or taxi trip duration
Tip: Still curious? For a detailed comparison between classification and regression, see this article.
Time-Series Forecasting
Forecast numeric values such as customer demands and stock prices in the future, based on historical time-stamped data.
Examples:
- Forecast stock prices for the next 30 days based on 6 months' historical data
- Forecast daily temperature for the next 10 days based on 3 months' historical data
Tip: Still curious? You can find an in-depth explanation of time series forecasting in this article.