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What is the difference between inference and prediction?
Inference: Use the model to learn about the data generation process. Prediction: Use the model to predict the outcomes for new data points. Since inference and prediction pursue contrasting goals, specific types of models are associated with the two tasks.
Can a ML model be used to make predictions?
You can deploy trained ML models for real-time or batch predictions on unseen data, a process known as inference. However, in most cases, the raw input data must be preprocessed and can’t be used directly for making predictions.
How to choose the best model for prediction?
Modeling: Consider several different models and different parameter settings. Model selection: Identify the model with the greatest predictive performance using validation/test sets; select the model with the highest performance on the test set.
Can a generalized linear model be used for inference?
Merely using a model that is suitable for inference does not mean that you are actually performing inference. What matter is how you are using the model. For example, although generalized linear models are suitable for inference, I recently used them solely for prediction purposes.
Is the response variable good enough to fix the skew?
My data contains some skewed features, and also the response variable (sale price) is also skewed. Log transforming all relevant features and the response variable is good enough and ‘fixes’ the skew.
How to use inference in data generation process?
Inference 1 Modeling: Reason about the data generation process and choose the stochastic model that approximates the data generation process best. 2 Model validation: Evaluate the validity of the stochastic model using residual analysis or goodness-of-fit tests. 3 Inference: Use the stochastic model to understand the data generation process .
Are there any working principles for inference problems?
For inference problems, on the other hand, the working principles of used models are well understood. In his famous 2001 paper, Leo Breiman argued that there are three revolutions in the modeling community, which are represented by the following terms:
Which is the best inference method for data generation?
Bayesian methods are particularly popular for inference because these models can be adjusted to incorporate various assumptions about the data generation process. Merely using a model that is suitable for inference does not mean that you are actually performing inference. What matter is how you are using the model.
What’s the difference between machine learning and prediction?
Note that machine learning is often concerned with predictive modeling, while the statistical community often relies on stochastic models that perform inference. Due to the complexity of machine learning models, they are often treated as black boxes.