Can someone solve MATLAB machine learning homework involving multicollinearity?
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I have worked with data for some time now. I think a multicollinearity problem is more common than you think. You’re right. Here’s an example: You are trying to predict a stock’s performance based on a combination of past data (from past quarterly earnings reports, for example) and future earnings predictions (from earnings for the next quarter). You’ve run tests to show that including each column of past data does not affect the predictive accuracy (assuming that each column is independent). However, when you add the future earnings data,
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Can you suggest an effective way to deal with multicollinearity in MATLAB’s machine learning framework?
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A multicollinearity error is a problem arising in the data analysis when the dependent variables (y) and the independent variables (X) have multiple independent coefficients in common (Kruskal et al. 2015). The reason is that a dependent variable could be calculated as the product of two or more independent variables. The multicollinearity error can lead to high standard errors and biased results in linear regression (Mohan and Sharma 2003). In this situation, the results obtained using the original equation may be significantly different from the correct ones
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In MATLAB, we have multicollinearity which occurs when two or more independent variables share a single coefficient. This means that a model’s coefficients for the independent variables become redundant. In this type of situation, the dependent variable can be regressed using multiple regression, but only the model coefficients that correspond to the covariates (independent variables) that contribute to the dependent variable can be estimated. Therefore, when there is multicollinearity, the model may contain some redundant terms. In MATLAB, we can check for multicollinearity using the cov method (
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When working with multicollinearity, it is essential to take a multicollinearity check before using any regression or classification algorithm. This check involves examining which variables are highly correlated and potentially collinear with one another. If two or more variables are highly correlated, then they become a single, large variable with multiple predictive powers. This can result in overfitting, meaning that the model may predict the wrong response for a given input variable. When a single variable is a good predictor, it’s called a principal component. Multicollinearity means that
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Can someone solve MATLAB machine learning homework involving multicollinearity? Yes, I can. MATLAB is a versatile tool for data analysis and statistical inference. In data science, MATLAB is used to solve complex analytical problems and to visualize and manipulate data for analysis. One of the most critical features of MATLAB is its built-in function for dealing with multicollinearity, a common problem that arises when analyzing a dataset. In the context of machine learning, multicollinearity occurs when the independent variable is correlated with multiple other independent
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You are currently browsing the Online Assignment Help blog archives for February, 2014. This assignment is a real-world scenario for machine learning, where a multivariate linear model is used to predict a dependent variable from several independent variables. The dependent variable (y) is the price for a specific item sold in a particular location, and the independent variables (x1, x2, x3, …, xn) represent attributes of the product that are used to model price. Here’s my solution: In my previous solution