Regularization 2

Using Matlab, please provide the script code necessary to find the result: By simulation, generate 10000 Random Samples (size n = 1) from an Exponential Distribution with µ = 10. Generate a histogram of the data and label it “n = 1”. Observe the shape of the histogram, which will be skewed right. Include the histogram in your report along with the mean and standard deviation of this sample of 10000 data points.

Plot following curves in the SAME figure where x-axis is "Model Complexity" and y-axis is the value: (1) Bias; (2) Variance; (3) Training Accuracy; (4) Testing Accuracy; [Paste your plot here]

Explain the flaws in this model training strategy. What's your solution? We want to create a hip X-Ray deformity prediction model. 100 individuals have 640 frontal X-rays. Three orthopedic physicians label the photos as positive or negative for hip deformity. The picture dataset was randomly divided among 80% training (training and validation) and 20% testing.

A high bias model has a high unpredictable error, while a high variance model has a high systematic error. Select: True or False?

This is a coding question. 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 showcase how well your model predicts against the observations. Use scatter plot tor 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 need to fine tune hyperparameters such as leurning rate and the number of iterations.

Take the Absenteeism at work Data Set, obtained from the UC-Irvine Machine Learning Data Repository, as a data set to be discretized. Perform data discretization for two of the numerical attributes using the Equal-Width or EqualFrequency method. (Let the stopping criteria be max-interval = 4). You need to write a small program to do this to avoid clumsy numerical computation. Submit your simple analysis and your test results: split points, final intervals, and your documented source program.

Only by seeing how well a model performs on test data can its accuracy be determined. elaborate on; provide additional information about Explain?

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 sct 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 showoase how well your model predicts against the observations. Use scatter plot for observations, line plot for your model predictions. Observations are in color red. and prodictions are in color green. Add appropriate labeis to the x axis and y axis and a title to the plot You may also need to fine tune hyperparameters such as Icurning rate and the number ofliterations.

k-Nearest Neighbor

We created some models for a dataset and, for each model we computed its R2 score. The results are presented in the table below: Model m1 m2 m3 m4 m5 R2 0.85 0.76 0.87 0.68 0.79 What model should we use from the ones presented in the table? Justify your answer. Answer:

Investigate each of the forty delivery time observations. Delete at random ten percent (4) of the observations. Calculate R2 and make a prediction based on the four values that were deleted. Recalculate 100 times. Estimate R2's value. What kind of impact does this have on the model's prediction? How does the mean of the 100R2 prediction values compare to the R2 that is derived from the PRESS for each of the 40 observations?

Explain this model training strategy's faults. Your solution? Create a hip X-Ray deformity prediction model. 100 people have 640 frontal X-rays. Three orthopedic surgeons rate the hip deformity images. The image dataset was randomly split across 80% training (training and validation) and 20% testing.

Compute the area under the curve (AUC) for this classifier and write it as a decimal number on [0,1]

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.

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.

The hyper-parameters of a model must NOT be tuned on the test data ( i.e, the data used to evaluate the performance of the final model after selecting the hyper-parameters) Group of answer choices True False

As a benchmark, use a normalized histogram. Is the absence of a non-normalized histogram in a normalized histogram acceptable?

This is a coding question. Try to program a Ridge regression. Please complete the coding. Note that here the data set we use has just one explanatory variable and the Ridgc regression we try to create here has just one variable (or feature). Now that you have finished the program. What are the obscrvations and the corresponding predictions using Ridge? Now, make a plot to showoase haw well your model predicts against the observations. Use scatter 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 need to tine tune hyperparamoters such as learning rate and the number of iterations. side note- make sure the code runs successfully

Question 4 Which one of the following statement is false regarding the purpose of K fold cross validation ? We will use all the training data to build a new model, using the best parameters found during the K fold cross validation To find the best model among the K models build during the process To avoid overfitting problem To find the model best parameters

need ans in 15 min.. Explain Intentionally Bias the Model to Over-Fit or Under-Fit with code?

What do standard deviation, range, and IQR tell us about a data set? Group of answer choices They tell us how many points would be needed to have a consistent data set. The tell us how much the data is spread out. The tell us the average height of a bar in the graph. They tell us where the general center of the data is.

Take the first 1000 entries of the training part of the Scikit-learn newsgroups (20 newsgroups) dataset (in the original order) as the training set, and set aside the next 100 entries as the test set. Use the first network from the 5th practice (provided below), make necessary modifications based on the data. Predict whether a post belongs to a political discussion group using a bag-of-words model (the category contains 'talk.politics'). What is the accuracy on the test set, what is the input_shape of the network, and what does the network predict for the last two entries of the test set? here is the model that is provided:  model = keras.Sequential() model.add(Dense(128,input_shape=(20000,))) model.add(Dense(64)) model.add(Dense(32)) model.add(Dropout(0.5)) model.add(Dense(4, activation='softmax')) how can i solve this?