Difference between revisions of "Test"

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==Introduction==
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===Feature Engineering==
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A feature cross is a machine learning technique that adds interaction features between features in a dataset. This technique can be used to capture non-linear relationships among features and improve performance of a model.
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[[Feature engineering]] in machine learning is the transformation of raw data into features that better reflect the underlying problem to the predictive model. This results in improved model accuracy using unseen data.
  
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==How it works==
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Using statistical analysis and domain knowledge, feature engineering identifies the relevant features that capture the underlying patterns of the data. This involves transforming and selecting the variables, creating new variables by combining or aggregating existing variable, and handling missing or incomplete information.
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A feature cross creates new features by combining two or more existing features. If we have two features, for example, x1 + x2, a feature crossover would create a new feature, which is x1 *x2. This new feature captures the interaction of x1-x2 and can improve a model's performance.
 
  
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==Examples==
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==Examples of Feature Engineering==
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A feature cross can be seen in the context polynomial regression. Polynomial regression allows us to add polynomial terms such as x12,x1*x2,x22, and so on. to capture non-linear relationships among the features.
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Here are some examples of feature engineering:
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- [[One hot encoding]] of categorical variable to convert them into numerical variables
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- [[Standardization]] numerical variables to ensure that they have a common scale
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- [[Feature Scaling]] to handle variables that have different units of measurement
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- [[Binning]] of numerical variable to convert them into categorical variables
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- [[Polynomial Features]] to capture nonlinear relationships between variables
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- [[Feature Selection]] to remove redundant or irrelevant features
  
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Another example is the context of natural-language processing (NLP), which allows us to use feature crosses to capture interaction between different words within a sentence. In the sentence "The cat sat on a mat", we can create new features to capture the interaction between "cat", "sat", or "on" and the "mat."
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==Why Feature Engineering is Important==
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Machine learning models can be significantly improved by using feature engineering. The model can learn more from the raw data by transforming it into features that better reflect the problem. The model can also be made to avoid overfitting by removing redundant or irrelevant features. This will allow it to perform better on unseen data and increase its generalization performance.
  
 
==Explain Like I'm 5 (ELI5)==
 
==Explain Like I'm 5 (ELI5)==
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A feature cross is a way to combine words. If you have the words "cat" or "sat", you could make a new word by adding them together. This can help a computer better understand the relationship between words, especially if they are related.
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Feature engineering is similar to making a puzzle. We start with a box full of puzzle pieces. This is raw data. Then we sort through the pieces and find the ones that make a nice picture. This is similar to making a prediction. You might also need to modify the shape of pieces or combine pieces together. This is similar to changing the raw data into something the computer can understand. This will allow the computer to make better predictions about things that it hasn't seen before.

Revision as of 09:51, 25 January 2023

=Feature Engineering

Feature engineering in machine learning is the transformation of raw data into features that better reflect the underlying problem to the predictive model. This results in improved model accuracy using unseen data.

Using statistical analysis and domain knowledge, feature engineering identifies the relevant features that capture the underlying patterns of the data. This involves transforming and selecting the variables, creating new variables by combining or aggregating existing variable, and handling missing or incomplete information.

Examples of Feature Engineering

Here are some examples of feature engineering: - One hot encoding of categorical variable to convert them into numerical variables - Standardization numerical variables to ensure that they have a common scale - Feature Scaling to handle variables that have different units of measurement - Binning of numerical variable to convert them into categorical variables - Polynomial Features to capture nonlinear relationships between variables - Feature Selection to remove redundant or irrelevant features

Why Feature Engineering is Important

Machine learning models can be significantly improved by using feature engineering. The model can learn more from the raw data by transforming it into features that better reflect the problem. The model can also be made to avoid overfitting by removing redundant or irrelevant features. This will allow it to perform better on unseen data and increase its generalization performance.

Explain Like I'm 5 (ELI5)

Feature engineering is similar to making a puzzle. We start with a box full of puzzle pieces. This is raw data. Then we sort through the pieces and find the ones that make a nice picture. This is similar to making a prediction. You might also need to modify the shape of pieces or combine pieces together. This is similar to changing the raw data into something the computer can understand. This will allow the computer to make better predictions about things that it hasn't seen before.


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