Machine Learning - Revision history

Rice: /* Classifications */

2024-04-26T06:49:57Z

Classifications

← Older revision		Revision as of 06:49, 26 April 2024
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	* [[Linear Regression]]		* [[Linear Regression]]
	* [[Polynomial Regression]]		* [[Polynomial Regression]]

			'''Clustering''' finds rules within a dataset.

	[[Category:Machine Learning]]		[[Category:Machine Learning]]

2024-04-26T06:16:06Z

Constructing ML Models

← Older revision		Revision as of 06:16, 26 April 2024
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	= Constructing ML Models =		= Constructing ML Models =
			A [[Dataset\|dataset]] is used to train ML models.

	The '''training dataset''' is the data used for training a model, containing a set of observations composed of a set of features. In a regression/classification model, we gain a hypothesis function <math>y = g(x)</math> such that it predicts the target variable ''y''.		The '''training dataset''' is the data used for training a model, containing a set of observations composed of a set of features. In a regression/classification model, we gain a hypothesis function <math>y = g(x)</math> such that it predicts the target variable ''y''.

2024-04-26T06:13:01Z

Flow

← Older revision		Revision as of 06:13, 26 April 2024
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	First, ''preprocessing'' (such as data cleaning and sampling) is done to make the data useful.		First, ''preprocessing'' (such as data cleaning and sampling) is done to make the data useful.

	Then, ''~~exploratory~~ data analysis (EDA),'' such as data visualization, allows us to understand the data and determine what types of algorithm to employ.		Then, ''[[Exploratory data analysis]] (EDA),'' such as data visualization, allows us to understand the data and determine what types of algorithm to employ.

	Thirdly, ''feature selection'' selects the important feature to reduce overfitting and improve accuracy of the model.		Thirdly, ''feature selection'' selects the important feature to reduce overfitting and improve accuracy of the model.

2024-04-26T01:03:18Z

Classifications

← Older revision		Revision as of 01:03, 26 April 2024
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	'''Classification''' produces a categorical output/label		'''Classification''' produces a categorical output/label
			* [[Binary classification]]

	'''Regression''' predicts a value.		'''Regression''' predicts a value.

2024-04-15T18:50:34Z

2024-04-03T18:51:33Z

Constructing ML Models

← Older revision		Revision as of 18:51, 3 April 2024
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	The '''test dataset''' is the data used to test the performance of the trained model.		The '''test dataset''' is the data used to test the performance of the trained model.

			= Classifications =
			Machine learning models is classified by the way they learn and the output they produce.

			'''Supervised learning''' is the process by which a model's output is checked to see if it matches the expectation in a known dataset, in the hopes that it will produce a good approximation for an unknown dataset.

			'''Unsupervised learning''' is the process by which a model attempt to find ''clusters''/patterns. It is used by Amazon to find trends.

			'''Classification''' produces a categorical output/label

			'''Regression''' predicts a value.
	[[Category:Machine Learning]]		[[Category:Machine Learning]]

2024-04-03T18:11:35Z

← Older revision		Revision as of 18:11, 3 April 2024
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	The '''test dataset''' is the data used to test the performance of the trained model.		The '''test dataset''' is the data used to test the performance of the trained model.
			[[Category:Machine Learning]]

2024-04-03T18:11:18Z

← Older revision		Revision as of 18:11, 3 April 2024
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	The '''test dataset''' is the data used to test the performance of the trained model.		The '''test dataset''' is the data used to test the performance of the trained model.

	~~[[Category:Computer Science]]~~

2024-04-01T18:43:17Z

← Older revision		Revision as of 18:43, 1 April 2024
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	First, ''preprocessing'' (such as data cleaning and sampling) is done to make the data useful.		First, ''preprocessing'' (such as data cleaning and sampling) is done to make the data useful.

	Then, ''exploratory data analysis (EDA)'' allows us to understand the data and determine what types of algorithm to employ.		Then, ''exploratory data analysis (EDA),'' such as data visualization, allows us to understand the data and determine what types of algorithm to employ.

			Thirdly, ''feature selection'' selects the important feature to reduce overfitting and improve accuracy of the model.

			= Constructing ML Models =
			The '''training dataset''' is the data used for training a model, containing a set of observations composed of a set of features. In a regression/classification model, we gain a hypothesis function <math>y = g(x)</math> such that it predicts the target variable ''y''.

			The '''test dataset''' is the data used to test the performance of the trained model.

	[[Category:Computer Science]]		[[Category:Computer Science]]

2024-04-01T18:38:32Z

← Older revision		Revision as of 18:38, 1 April 2024
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	Rule-based systems follows a set of pre-defined rules defined by experts to cover all scenarios to automate the decision making process. This is not sufficient for complex systems. '''Machine learning (ML)''' builds models to identify and predict patterns, make decisions, and automate processes.		Rule-based systems follows a set of pre-defined rules defined by experts to cover all scenarios to automate the decision making process. This is not sufficient for complex systems. '''Machine learning (ML)''' builds models to identify and predict patterns, make decisions, and automate processes.

			= Flow =
			Unstructured: Images, sentences

			'''Unstructured data''' is not understood by machines without some algorithms to process it. '''Structured data''' is machine-readable.

			First, ''preprocessing'' (such as data cleaning and sampling) is done to make the data useful.

			Then, ''exploratory data analysis (EDA)'' allows us to understand the data and determine what types of algorithm to employ.

			[[Category:Computer Science]]