If minimax equilibrium is reached, what is the probability with which the discriminator in a GAN model will discriminate real data from fake data?
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- Can coefficients of an AR(p) be estimated by OLS? Explain how estimated regression function can be used for forecasting?PCA tried to find new basis vectors (axes) that maximize the variance of the instances. Is True or False?The following are all benefits of generalized additive models (GAMS), EXCEPT: GAMS are less computationally demanding than linear regression. GAMS can model non-linear relationships that standard linear regression will miss. GAMS can potentially make more accurate predictions of the response than linear regression can. One can examine the effect of each predictor on the response individually while holding all of the other predictors fixed.
- Now suppose that you have two versions of your model with different parameters(e.g., different regularization) or even different model families (e.g., logistic regression versus random forest). Which one is better?What is the mean of Linear Correlation ? if r = 0 has strong linear correlation it does mean has strong linear relationship?How many parameters can be automatically tuned in Linear Regression with Elastic Net Regularization? Group of answer choices 0 2 3 1
- Draw a gaussian curve, including the probabilities of the areas under the gaussian curve, and describe the characteristics of a normally distributed dataset by relating measures of central tendency, the measures of dispersion, kurtosis, and skewness with each other.CLEAR DRAWING. OWN WORK FOR UPVOTEWe want to train a binary Logistic Regression classifier for the given trainingdataset:Explain what autocorrelation indicates . What are the main problems that autocorrelation creates for OLS estimation results ? Give two ways to detect autocorrelation problem and the hypothesis that are tested ?
- What is the difference between one-vs-rest binary logistic regression and multinomial logistic regression and MNLogit ?"When conducting a binary regression with a skewed predictor, it is often easiest to assess the need for x and log(x) by including them both in the model so that their relative contributions can be assessed directly." Show that indeed the log odds are a function of x and log(x) for the gamma distribution.A Ridge Linear Regression adds the sum of the squared values of the coefficients to the loss function to penalize large coefficients. Group of answer choices True False