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| computer_science:machine_learning:types_of_machine_learning [2020/07/23 11:33] – [Clustering Problem:] carlossousa | computer_science:machine_learning:types_of_machine_learning [2023/12/01 12:07] (current) – external edit 127.0.0.1 | ||
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| Supervised learning is the most common and studied type of learning because it is easier to train a machine to learn with labeled data than with un-labeled data. Depending on what you want to predict, supervised learning can used to solve two types of problems: regression or classification. | Supervised learning is the most common and studied type of learning because it is easier to train a machine to learn with labeled data than with un-labeled data. Depending on what you want to predict, supervised learning can used to solve two types of problems: regression or classification. | ||
| - | ===== Regression | + | ==== Regression ==== |
| If you want to predict continuous values, such as trying to predict the cost of a house or the weather outside in degrees, you would use regression. This type of problem doesn’t have a specific value constraint because the value could be any number with no limits. | If you want to predict continuous values, such as trying to predict the cost of a house or the weather outside in degrees, you would use regression. This type of problem doesn’t have a specific value constraint because the value could be any number with no limits. | ||
| - | ===== Classification | + | ==== Classification ==== |
| If you want to predict discrete values, such as classifying something into categories, you would use classification. A problem like, “Will he make this purchase” will have an answer that falls into two specific categories: yes or no. This is also called a, binary classification problem. | If you want to predict discrete values, such as classifying something into categories, you would use classification. A problem like, “Will he make this purchase” will have an answer that falls into two specific categories: yes or no. This is also called a, binary classification problem. | ||
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| ===== Unsupervised Machine Learning ===== | ===== Unsupervised Machine Learning ===== | ||
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| Depending on what you want to group together, unsupervised learning can group data together by: clustering or association. | Depending on what you want to group together, unsupervised learning can group data together by: clustering or association. | ||
| - | ===== Clustering Problem | + | ==== Clustering Problem ==== |
| Unsupervised learning tries to solve this problem by looking for similarities in the data. If there is a common cluster or group, the algorithm would then categorize them in a certain form. An example of this could be trying to group customers based on past buying behavior. | Unsupervised learning tries to solve this problem by looking for similarities in the data. If there is a common cluster or group, the algorithm would then categorize them in a certain form. An example of this could be trying to group customers based on past buying behavior. | ||
| - | + | ==== Association Problem ==== | |
| - | ===== Association Problem | + | |
| Unsupervised learning tries to solve this problem by trying to understand the rules and meaning behind different groups. Finding a relationship between customer purchases is a common example of an association problem. Stores may want to know what type of products were purchased together and could possibly use this information to organize the placement of these products for easier access. One store found out that there was a strong association between customers buying beer and diapers. They deduced from this statement that males who had went out to buy diapers for their babies also tend to buy beer as well. | Unsupervised learning tries to solve this problem by trying to understand the rules and meaning behind different groups. Finding a relationship between customer purchases is a common example of an association problem. Stores may want to know what type of products were purchased together and could possibly use this information to organize the placement of these products for easier access. One store found out that there was a strong association between customers buying beer and diapers. They deduced from this statement that males who had went out to buy diapers for their babies also tend to buy beer as well. | ||