Assume we are using regularized logistic regression for binary classification. Assume you have observed very large errors with its predictions on the new (unseen) data point, while the model has a low error on training data. Which of the following are steps would help to reduce the error on unseen data? Check all that apply. Use fewer training examples. Try adding polynomial features. O Try using a smaller set of features. Get more training examples.

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.

PCA tried to find new basis vectors (axes) that maximize the variance of the instances. Is True or False?

All-subsets regression is superior than stepwise regression in terms of data analysis, therefore explain why this is so.

If you are implementing regularised linear regression and when you tested your hypothesis in a new dataset you found that it suffer from high variance. How can you rectify the model ? Give 3 Solutions

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.

Make sure to show it as steps on a python with the final answer Fit a decision tree model using the training dataset (`x_train` and `y_train`)Create a variable named `y_pred`. Make predictions using the `x_test` variable and save these predictions to the y_pred variable Create a variable called `dt_accuracy`. Compute the accuracy rate of the logistic regression model using the `y_pred` and `y_test` and assign it to the `dt_accuracy` variable

Please provide steps to answer this question: Your task is simple: use machine learning to create a model that predicts which passengers survived the Titanic shipwreck. Implement and train a classification model for the Titanic dataset (the dataset can be found here: https://www.kaggle.com/c/titanic). Please ignore the test set (i.e., test.csv) and consider the given train set (i.e., train.csv) as the dataset. What you need to do: 1. Data cleansing 2. Split the dataset (i.e., train.csv) into a training set (80% samples) and a testing set (20% samples) 3. Train your model (see details below) 4. Report the overall classification accuracies on the training and testing sets 5. Report the precision, recall, and F-measure scores on the testing set

Define regression testing. Also define, (a)Baseline version (b)Delta version (c) Delta build.

You trained the regression model with 100 regressors and 1000 observations in the training and another 1000 in the test sample. You found that in-sample R2 over the training sample is 70% and the out-of-sample R2 over the test sample only - 30%. (select all that apply) a) Do you think there is any problem and how would you characterize it? Can adding more regressors (if you have them) help the model? b) Which approaches you may use to solve the problem? c) What would you expect the in-sample R2 to increase or decrease after that? What about the out-of-sample (test) R2?