Match the following terms that relate to minimizing a cost function. Loss/Cost function Error/residual Objective function [Choose ] Measures the total error over all the data A function that we want to optimize when training using algorithms like gradient descent. Measures the penalty for a given data point between the prediction and label [Choose]
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- GD algorithm Consider Linear Regression with single variable (univariate) problem. What will be the (approximate if can’t say accurately) values of derivatives of cost/loss function ‘J’ w.r.t. all the parameters by considering one at a time, and why? What is the significance and/or usage of these θj* for the cost function ‘J’ and hypothesis ‘h’? Given a dataset where first column is the label ‘y’ while other columns represent factors ‘xi’ as follows: X = [ 1 0 1 0 1 0 ] Using GD algorithm, find the linear model. Show all the calculationsQuestion: What are Marginal Effects and interpret the coefficients. NOTE: We need a short description of 3/4 sentences of the above.Please help with artificial intelligence questuon below If given a number of data samples from pairs of values (x, y) namely {(0, 12), (1, 19), (2, 29), (3, 37), (4, 45)} (1) Make a data plot from the given sample where the vertical axis is the y value and the horizontal axis is the x value. (2) Given a simple regression model: y = a x + b where a, b are constant numbers. Use the given data sample and least square method to predict constants a and b from the regression model. (3) Using the predicted regression model in question (2) above, calculate the estimated value of y if the value of x = 7.
- Please match the following situations with a more likely problem of a high bias or a high variance. Overfitting Both training performance and testing performance are very poor. Underfitting The model did a perfect prediction on the training but did poorly on the testing.2. When evaluating Bootstrap estimates, say 1000 estimates of R squared, what is the source of variation?This is a coding question. Now that you have worked out the gradient descent and the update rules. "Try to progrum a Ridge regression. Please complete the coding. Note that here the data set we use has just one explanatory variable and the Ridge regression we try to create here has just one variable (or feature). Now that you have finished the program. What are the observations and the corresponding predictions using Ridge? Now, make a plot to showease how well your model predicts against the observations. Use spatter plot for observations, line plot for your model predictions. Observations are in color red. and predictions are in color green. Add appropriate labels to the x axis and y axis and a title to the plot You may also nood to fine tune hyperparameters such as leurning rate and the number of'aterations.
- Step 1. Intersection over Union # def intersection_over_union(dt_bbox, gt_bbox): ---> return iou Step 2. Evaluate Sample We now have to evaluate the predictions of the model. To do this, we will write a function that will do the following: Take model predictions and ground truth bounding boxes and labels as inputs. For each bounding box from the prediction, find the closest bounding box among the answers. For each found pair of bounding boxes, check whether the IoU is greater than a certain threshold iou_threshold. If the IoU exceeds the threshold, then we consider this answer as True Positive. Remove a matched bounding box from the evaluation. For each predicted bounding box, return the detection score and whether we were able to match it or not. def evaluate_sample(target_pred, target_true, iou_threshold=0.5): # ground truth gt_bboxes = target_true['boxes'].numpy() gt_labels = target_true['labels'].numpy() # predictions dt_bboxes =…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?Please use Housingdataset.csv file. Build a Transaction Type predictive model using 3 different learning algorithms (e.g. KNN, MLP, SVM) Complete the table below. Accuracy Precision Recall Logistic Regression MLP SVM Briefly explain your methodology and the implication of your result. For a full credit, please include all relevant mathematical formulars and diagrams.
- Discuss the benefits of using a data model in the context of the standard method for making strength predictions.Write a computer code to do linear regression analysis of a given dataset to find the relation between two variables which gives the least sum of squares error. Using Excel or Matlab. Include a copy of the script, a sample input, and a sample output from your codes. Sample output must include modelfit parameters and the sum of the squared errors for the fit. Please also run the code for thefollowing data set to find and report the relation between y and x:x y25 3040 80120 15075 80150 200300 350270 240400 320450 470575 583Consider a logistic regression classifier with the following weight vector: [2, 5, -10,0, -1], and the following feature vector: [0,1,1,3,-5] . Let b=0. Compute the score assigned by the classifier to the positive class. Assume the correct label for this example is POS. Compute the cross-entropy loss of the function on this example. Now assume the correct label is NEG. Compute the cross-entropy loss