New answers tagged regression
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How do we distinguish between correlated and un-correlated features/variables ? Is it relevant for a regression analysis?
The word "correlated" is an adjective and indicates "loose" association between two variables i.e. it does not indicate a (significant) causal relationship. For example, chi-...
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Get the Polynomial Equation with Two Variables in Python
Your dependant variable (price) needs to be on the Y-axis and your independent variable (length) needs to be on the X-axis. The resulting equation (if polynomial) will then output price when you enter ...
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What are the constants in this formula for polytrophic head?
I'm not very aware of this matter, but since you know that
can't you just create 3 variables $H_1, H_2, H_3$ with random parameters and than doing a system of equations with $b_1, b_2, b_3$ as ...
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Maximizing minimum correlation
This technique is performed when the problem asks to create a variable composed of many highly-correlated independent variables.
Sometimes, when a variable can be linearly predicted from others, the ...
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Why is my predictor value (continuous) perfectly correlated with my logit value (when testing logistic regression model assumptions)?
$$
\text{logit}=\hat\beta_0+\hat\beta_1x\\
\text{cor}(x, \text{logit})\\
=\text{cor}(x, \hat\beta_0+\hat\beta_1x)\\
=\text{cor}(x, \hat\beta_1x)
$$
If the estimated slope coefficient $\hat\beta_1>0$...
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How do we distinguish between correlated and un-correlated features/variables ? Is it relevant for a regression analysis?
Yes! Correlation among features/attributes is indeed relevant to the regression analysis.
Correlation is the degree to which two or more features are associated with each other or exhibit some form of ...
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Removing constant from the regression model
The regression constant is an output part. You should not ignore it. Further, your interpretation of summary outputs is invalid. The regression coefficient of the independent variable is highly ...
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Understanding the computation of Standard Error of Regression Coeficients (ISLR)
Variance symbol (σ2) in the formulas is the variance of sampling error. And occasionally denoted as Var(ε). it is assumed that sampling error is unrelated to common variance.
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How to use time-sequence data with "meta data" of single value per sample?
My impression is that this is as much an instrumentation problem as a DS/ML one. Without knowing the details of your setup (geometry, position of the electrodes, number of fishes in the tank at once ...
- 449
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Accepted
Can I change the number of inputs to a keras model while preserving the trained existing weights
One way to do this is to create a second model with your new inputs and the same number/size of hidden layers and output layer, then copy the weights from the layers of your first model to the second ...
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