Abacus.AI - Evaluating Predictions

Overview Use Cases Customer Churn Prediction Time Series Anomaly Detection Event Anomaly Detection Cloud Spend Alerts Personalized Promotions Predictive Modeling Real-Time Forecasting Financial Metrics Forecasting Demand Forecasting Cumulative Forecasting Feature Group Requirements Training Models Evaluating Predictions Predictions Text extraction and classification NLP Powered Search Sentiment Analysis Finetuned LLM ChatLLM Language Detection Image Classification & Detection Object Detection Clustering Timeseries Clustering Sales and Revenue Forecasting Predictive Lead Scoring Personalized Search Personalized Recommendations Related Items Model Drift and Monitoring Tensorflow with Vector Matching Custom Python Model Data Ingestion Streaming Feature Store Vector Store AI Agents Named Entity Recognition Guidelines Optimization Connectors Authentication Getting Started with the Python SDK API Reference API Classes API Inputs Documentation Chat Bot API Search Video Tutorials ChatLLM

Problem type: Cumulative Forecasting

Forecasting is the process of

Quick Evaluation Recipe

Compare metric scores with the baseline scores to see if there's an improvement or not

Forecasting

Start with accuracy and point-wise accuracy, if there is a significant increase in both as compared to baseline then the model is learning useful patterns. The percentage increase varies greatly from case to case and is dependent on a variety of factors

If all of the above things look good then the model is good to go and you have trained a world-class deep learning-based forecasting model in just a few minutes with only a few clicks.