What is training and testing data?
Training data and testing data are essential components in building and evaluating predictive models:
- Training Data: Training data is used to train the predictive model. It consists of a set of input-output pairs, where the input (independent variables) is used to predict the output (dependent variable). The model learns the patterns and relationships in the training data to make predictions. It’s crucial to have a diverse and representative training dataset to ensure that the model generalizes well to new, unseen data.
- Testing Data: Testing data is used to evaluate the performance of the trained model. It consists of a separate set of input-output pairs that were not used during the training process. The model makes predictions on the testing data, and the predictions are compared to the actual values to assess the model’s performance. Testing data helps estimate how well the model will perform on new, unseen data.
Splitting the dataset into training and testing sets is typically done randomly, with a certain percentage of the data allocated to each set. Common splits include 70% training data and 30% testing data or 80% training data and 20% testing data. It’s important to ensure that the distribution of the data is maintained in both sets to avoid bias in the evaluation of the model.
What is Predictive Modeling ?
Predictive modelling is a process used in data science to create a mathematical model that predicts an outcome based on input data. It involves using statistical algorithms and machine learning techniques to analyze historical data and make predictions about future or unknown events.
Table of Content
- What is predictive modelling?
- Importance of Predictive Modeling
- Applications of Predictive Modeling
- What are dependent and independent variables?
- How to select the Right model?
- What is training and testing data?
- Types of Predictive Models
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