Define Soft-Margin explaining how much Bias and Variance each tend to have. You could contrasted to Maximal-Margin-Classifier, and its trend on Bias and Variance.
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Define Soft-Margin explaining how much Bias and Variance each tend to have.
You could contrasted to Maximal-Margin-Classifier, and its trend on Bias and Variance.
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- A high bias model has a high unpredictable error, while a high variance model has a high systematic error. Select: True or False?What is the mean of Linear Correlation ? if r = 0 has strong linear correlation it does mean has strong linear relationship?Stationarity of a Moving Average Model automatically follows from stationarity of the error terms. Claim that whether it is true or false and explain it.
- When we discuss measures of center and spread, there are certain ones that go together, and certain ones that do not. If we talk about the center in terms of the mean, then spread is described using the ["", ""] . If we talk about the center in terms of the median, then the spread is described using the ["", ""] .What is the difference between variance and unconditional variance. Can you give an example and if you know why it is used in the ARCH and GARCH modelsLike the Hough transform, RANSAC can be used to identify the best fitting line to a group of points. Given a set of 12 points, how many points are used to create a line model? How is a line model scored to determine whether it is a good fit? What makes RANSAC more robust to outliers?
- 1. Explain Logistic Regression with an example. Derive the cost function fromsigmoid to log loss.2. What is bias, variance, underfitting, and overfitting? Explain the Bias-VarianceTradeoff with figures.3. What is Boosting? Explain AdaBoost in detail with figures.4. Explain cross validation and its workflow? Explain k-fold cross validation, andwrite the pseudocode of k-fold cross validation5. Explain Ridge and Lasso Regression?6. What is maximum likelihood? Discuss Softmax Regression?cements Suppose we use Adaboosting with linear classifiers to classify between red and green ents points, and in iteration 1, the classifier chosen is as shown in the Figure (the decision boundary is indicated by dotted line and the bold text indicates the label assigned to either ons side of the boundary). Which of (a), (b) or (c) is most likely to be the next classifher chasen in the ensemble. (Note: no numerical computations needed to answer this) RED teration 1 RED GREEN RED GREEN ons RED GREEN (b) (a)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 ?
- PCA tried to find new basis vectors (axes) that maximize the variance of the instances. Is True or False?Analyze the dataset given in image of Infection forecast to classify the patient to Infected or not. Use ID3 algorithm or Identification tree and Calculate Entropy and Information Gain of each attribute to identify root node of the decision tree and its branches.What are the major differences in use-cases for linear regression as compared with non-linear types?