Model Types

Regression, time series forecasting, and classification are three common types of machine learning models that Akkio can generate. Here's how each of them work:
  1. 1.
    Regression: Use to predict a continuous numerical value. A regression model learns the relationship between input variables and a continuous output variable, such as predicting a person's salary based on their years of experience, age, education level, etc. Regression models are trained using labeled data, where the correct outputs are known, and can be used for tasks such as predicting housing prices, stock prices, or the likelihood of a customer buying a product.
  2. 2.
    Time Series Forecasting: Time series forecasting predicts future values of a variable over time, given historical data. This is often used in applications such as weather forecasting, stock market analysis, and energy demand forecasting. Models are trained using historical data and aim to identify patterns or trends in the data to make accurate predictions about future values. Common algorithms used for time series forecasting include ARIMA, SARIMA, and LSTM neural networks.
  3. 3.
    Classification: Machine learning uses classification to predict a categorical label or class. A classification model learns the relationship between input variables and a discrete output variable, such as classifying an email as spam or not spam, or predicting whether a customer is likely to churn or not. Classification models are trained using labeled data and can be used for tasks such as image recognition, sentiment analysis, or fraud detection. Common algorithms used for classification include logistic regression, decision trees, random forests, support vector machines (SVM), and deep neural networks.

Insights Reports

Each model type generates a unique Insights Report. These are detailed in this section.