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Can Ann perform the task of logistic regression?
Training an ANN is analogous to estimating parameters in a logistic regression model; however, an ANN is not an automated logistic regression model because the two models use different training algorithms for parameter estimation.
Which is better neural network or logistic regression?
Compared to logistic regression, neural network models are more flexible, and thus more susceptible to overfitting. Network size can be restricted by decreasing the number of variables and hidden neurons, and by pruning the network after training.
Is logistic regression A Perceptron?
In some cases, the term perceptron is also used to refer to neural networks which use a logistic function as a transfer function (however, this is not in accordance with the original terminology). In that case, a logistic regression and a “perceptron” are exactly the same.
When does overfitting occur in a regression analysis?
Overfitting a model is a condition where a statistical model begins to describe the random error in the data rather than the relationships between variables. This problem occurs when the model is too complex. In regression analysis, overfitting can produce misleading R-squared values, regression coefficients, and p-values.
When do you need more observations in a regression model?
For instance, if the regression model has two independent variables and their interaction term, you have three terms and need 30-45 observations. Although, if the model has multicollinearity or if the effect size is small, you might need more observations.
When to use Adjusted R-squared in regression analysis?
Adjusted R-Squared is used only when analyzing multiple regression output and ignored when analyzing simple linear regression output. When we have more than one independent variable in our analysis, the computation process inflates the R-squared.
What does significance F mean in regression output?
Statistically speaking, the significance F is the probability that the null hypothesis in our regression model cannot be rejected. In other words, it indicates the probability that all the coefficients in our regression output are actually zero!