# Goodness-of-fit

## #Model Selection

Does the data agree with the model?

- Calculate the distance between data and model predictions.
- Apply Bayesian methods such as likelihood estimation: likelihood of observing the data if we assume the model; the results will be a set of fitting parameters.
- …

Why don’t we always use goodness-of-fit as a measure of the goodness of a model?

- We may experience overfitting.
- The model may not be intuitive.

This is why we would like to balance it with parsimony using some measures of generalizability.

Lei Ma (2020). 'Goodness-of-fit', Datumorphism, 11 April. Available at: https://datumorphism.leima.is/wiki/model-selection/goodness-of-fit/.

**Current Ref:**

- wiki/model-selection/goodness-of-fit.md

**Links to:**

###### Model Selection

Suppose we have a generating process that generates some numbers based on a distribution. Based on a …

###### Parsimony of Models

For models with a lot of parameters, the goodness-of-fit is very likely to be very high. However, it …

###### Measures of Generalizability

To measure the generalization, we define a generalization error, $$ \begin{align} \mathcal G = …

###### Likelihood

Likelihood is not necessarily a pdf

**Links from:**

###### Model Selection

Suppose we have a generating process that generates some numbers based on a distribution. Based on a …

###### Parsimony of Models

For models with a lot of parameters, the goodness-of-fit is very likely to be very high. However, it …

###### MDL and Neural Networks

Minimum Description Length ( MDL Minimum Description Length MDL is a measure of how well a model …

###### Akaike Information Criterion

Suppose we have a model that describes the data generation process behind a dataset. The …

###### Bayesian Information Criterion

BIC considers the number of parameters and the total number of data records.