Curve fitting - Revision history

Rice: Rice moved page Curve Fitting to Curve fitting

2024-05-18T19:48:53Z

Rice moved page Curve Fitting to Curve fitting

← Older revision		Revision as of 19:48, 18 May 2024
(No difference)

Rice at 19:31, 17 May 2024

2024-05-17T19:31:33Z

← Older revision		Revision as of 19:31, 17 May 2024
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	This avoids the problem of model only targeting training data		This avoids the problem of model only targeting training data

			[[Category:Machine Learning]]

Rice: /* Overfitting */

2024-04-26T06:57:46Z

Overfitting

← Older revision		Revision as of 06:57, 26 April 2024
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	Overfitting leads to a model not responding well to input outside of the training data.		Overfitting leads to a model not responding well to input outside of the training data.

			There are two main ways to detect/discourage overfitting

			* [[Regularization\|Lasso regression]]
			* [[Ridge Regression\|Ridge regression]]

	= Error of the model =		= Error of the model =

Rice at 18:37, 15 April 2024

2024-04-15T18:37:38Z

← Older revision		Revision as of 18:37, 15 April 2024
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	If a model has ''high bias'' and ''low variance'', the model undefits the data		If a model has ''high bias'' and ''low variance'', the model undefits the data

			= Overfitting =
			'''Overfitting''' occurs when the model is too complex, such as a high degree in polynomial regression.

			Overfitting leads to a model not responding well to input outside of the training data.

	= Error of the model =		= Error of the model =

Rice at 18:54, 8 April 2024

2024-04-08T18:54:25Z

@@ Line 1: / Line 1: @@
 '''Curve fitting''' is the process of defining/determining (''fit'') a function (''curve'') that best approximates the relationship between dependent and independent variables.
-* '''Underfitting''' is when models are too basic
 = Underfitting =
@@ Line 10: / Line 9: @@
 = Error of the model =
 <math>\text{Error of the model} = \text{Bias}^2 + \text{Irreducible Error}</math>
 If a model has high bias and low variance, the model undefits the data. If the opposite occurrs, it overfits.
@@ Line 30: / Line 37: @@
 <math>MSE = \frac{1}{n} \sum (y_i - \hat{y_i})^2</math>
-It can be used to measure the fit of the model on the training and test data.
+It can be used to measure the fit of the model on the training and test data. It is not a normalized value, so we use it in conjunction with another value to get the error in terms of percentage.

Rice: Created page with "'''Curve fitting''' is the process of defining/determining (''fit'') a function (''curve'') that best approximates the relationship between dependent and independent variables. * '''Underfitting''' is when models are too basic * '''Overfitting''' is when models are too complex, which may lead to incorrect predictions for values outside of the training data. = Underfitting = Visualize the fit of the model on the test data and bias variance tradeoff. If a model has ''hi..."

2024-04-08T18:40:45Z

Created page with "'''Curve fitting''' is the process of defining/determining (''fit'') a function (''curve'') that best approximates the relationship between dependent and independent variables. * '''Underfitting''' is when models are too basic * '''Overfitting''' is when models are too complex, which may lead to incorrect predictions for values outside of the training data. = Underfitting = Visualize the fit of the model on the test data and bias variance tradeoff. If a model has ''hi..."

New page

'''Curve fitting''' is the process of defining/determining (''fit'') a function (''curve'') that best approximates the relationship between dependent and independent variables.

* '''Underfitting''' is when models are too basic
* '''Overfitting''' is when models are too complex, which may lead to incorrect predictions for values outside of the training data.

= Underfitting =
Visualize the fit of the model on the test data and bias variance tradeoff.

If a model has ''high bias'' and ''low variance'', the model undefits the data

= Error of the model =
<math>\text{Error of the model} = \text{Bias}^2 + \text{Irreducible Error}</math>
If a model has high bias and low variance, the model undefits the data. If the opposite occurrs, it overfits.

== Bias Variance Tradeoff ==
[[File:BiasVarianceTradeoff-scott fortmann-roe com.png|thumb|Bias Variance Tradeoff, https://scott.fortmann-roe.com/docs/BiasVariance.html]]
'''Bias''' is the error between average model prediction and the ground truth

<math>\text{Bias}^2 = E [(E[g(x)] - f(x))^2 ]</math>

'''Variance''' is the error between the average model prediction and the model prediction

<math>\text{Variance} = E [(E[g(x)] - g (x))^2 ]</math>

Bias and variance have an inverse relationship.

== Regression Models ==
'''Mean squared error''' uses the mean of a ''collection of differences'' between the prediction and the truth squared as a ''measurement for fit.''

<math>MSE = \frac{1}{n} \sum (y_i - \hat{y_i})^2</math>

It can be used to measure the fit of the model on the training and test data.