Cross-Validation - Revision history

BPeat: Text replacement - "http://" to "https://"

2023-03-28T11:54:56Z

Text replacement - "http://" to "https://"

BPeat at 14:34, 30 September 2020

2020-09-30T14:34:29Z

BPeat at 00:08, 28 September 2020

2020-09-28T00:08:37Z

BPeat at 18:00, 17 August 2020

2020-08-17T18:00:14Z

BPeat at 01:59, 2 June 2020

2020-06-02T01:59:23Z

BPeat at 01:56, 2 June 2020

2020-06-02T01:56:54Z

BPeat at 01:55, 2 June 2020

2020-06-02T01:55:46Z

BPeat at 13:42, 30 May 2020

2020-05-30T13:42:44Z

BPeat at 13:32, 30 May 2020

2020-05-30T13:32:17Z

BPeat at 13:21, 30 May 2020

2020-05-30T13:21:43Z

@@ Line 5: / Line 5: @@
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
-[http://www.youtube.com/results?search_query=Cross+Validation+machine+learning+ML YouTube search...]
+[https://www.youtube.com/results?search_query=Cross+Validation+machine+learning+ML YouTube search...]
-[http://www.google.com/search?q=Cross+Validation+machine+learning+ML ...Google search]
+[https://www.google.com/search?q=Cross+Validation+machine+learning+ML ...Google search]
 * [[Algorithm Administration#Automated Learning|Automated Learning]]
@@ Line 12: / Line 12: @@
 * [[Feature Exploration/Learning]]
 * [[Recursive Feature Elimination (RFE)]]
-* [http://docs.aws.amazon.com/machine-learning/latest/dg/cross-validation.html Cross-Validation | AWS]
+* [https://docs.aws.amazon.com/machine-learning/latest/dg/cross-validation.html Cross-Validation | AWS]
 * [[Evaluating Machine Learning Models]]
 * [[Monte Carlo]]
@@ Line 18: / Line 18: @@
-a technique for evaluating Machine Learning (ML) models by training several ML models on subsets of the available input data and evaluating them on the complementary subset of the data. Use cross-validation to detect overfitting, ie, failing to generalize a pattern [http://docs.aws.amazon.com/machine-learning/latest/dg/cross-validation.html Developer Guide - Amazon AWS ML]
+a technique for evaluating Machine Learning (ML) models by training several ML models on subsets of the available input data and evaluating them on the complementary subset of the data. Use cross-validation to detect overfitting, ie, failing to generalize a pattern [https://docs.aws.amazon.com/machine-learning/latest/dg/cross-validation.html Developer Guide - Amazon AWS ML]
 * Method of estimating expected prediction error

← Older revision		Revision as of 14:34, 30 September 2020
Line 15:		Line 15:
	* [[Evaluating Machine Learning Models]]		* [[Evaluating Machine Learning Models]]
	* [[Monte Carlo]]		* [[Monte Carlo]]
		+	* [[Backtesting]]

@@ Line 8: / Line 8: @@
 [http://www.google.com/search?q=Cross+Validation+machine+learning+ML ...Google search]
-* [[Automated Machine Learning (AML) - AutoML]]
+* [[Algorithm Administration#Automated Learning|Automated Learning]]
 * [[Principal Component Analysis (PCA)]]
 * [[Feature Exploration/Learning]]

← Older revision		Revision as of 18:00, 17 August 2020
Line 14:		Line 14:
	* [http://docs.aws.amazon.com/machine-learning/latest/dg/cross-validation.html Cross-Validation \| AWS]		* [http://docs.aws.amazon.com/machine-learning/latest/dg/cross-validation.html Cross-Validation \| AWS]
	* [[Evaluating Machine Learning Models]]		* [[Evaluating Machine Learning Models]]
		+	* [[Monte Carlo]]

← Older revision		Revision as of 01:59, 2 June 2020
Line 13:		Line 13:
	* [[Recursive Feature Elimination (RFE)]]		* [[Recursive Feature Elimination (RFE)]]
	* [http://docs.aws.amazon.com/machine-learning/latest/dg/cross-validation.html Cross-Validation \| AWS]		* [http://docs.aws.amazon.com/machine-learning/latest/dg/cross-validation.html Cross-Validation \| AWS]
		+	* [[Evaluating Machine Learning Models]]

@@ Line 17: / Line 17: @@
 a technique for evaluating ML models by training several ML models on subsets of the available input data and evaluating them on the complementary subset of the data. Use cross-validation to detect overfitting, ie, failing to generalize a pattern [http://docs.aws.amazon.com/machine-learning/latest/dg/cross-validation.html Developer Guide - Amazon AWS ML]
-<youtube>pcZ4YlvhSKU</youtube>
+* Method of estimating expected prediction error
-<youtube>7RiZFKMS3cI</youtube>
+* Helps selecting the best fit model
-<youtube>MYnxxRoPiwI</youtube>
+* Help ensuring model is not over fit
-<youtube>xlHk4okO8Ls</youtube>