Hello I need help with following python problem.
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A: Given: What is python? Why was python created and why do people use python?
Q: Python
A: Answer is in given below:-
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A: #see the below code
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Q: (Python) Without giving me the code, can you give me some advice or tips on approaching this?
A: The solution is given in next step:-
Hello I need help with following python problem.. Thank you in advance..!
![1g) Defining the train & test sets
Now, we'll instead use sklearn 's train_test_split() function here to define our train
and test set. Store train data (predictors) into MR_train_X and labels (outcomes) into
MR_train_Y. Similarly, store test data into MR_test_X and test labels into MR_test_Y .
In addition to providing the predictors ( MR_X ) and outcomes ( MR_Y ) to the function, we will
use the following arguments for this task:
test_size : 0.2
random_state : 200
1 # YOUR CODE HERE
MR_train_Y.shape [0]
MR_test_Y.shape [0]
1 assert MR_train_X.shape [0]
2 assert MR_test_X. shape [0]
3
4 assert len (MR_train_X)
5 assert len(MR_test_Y)
4000
1000](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F25ecc335-25be-4ebd-8f8c-629ee64b2629%2F74d70933-b146-425b-8e6b-e36999640000%2Fzor61fh_processed.png&w=3840&q=75)
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- Use an appropriate scikit-learn library we used in class to create y_train, y_test, X_train and X_test by splitting the data into 70% train and 30% test datasets. Set random_state to 4 and stratify the subsamples so that train and test datasets have roughly equal proportions of the target's class labels Standardise the data using StandardScaler libraryPlease send me answer within 10 mins I will upvote your answer for must. Python: Run a paired bootstrap test to compare the means e1 and e2. I was able to make a bootstrap test to find whether or not two datasets have significantly different means but I'm not sure what to add to make it a paired bootstrap. The paired bootstrap should look exactly like this but with one added component. What would need to be added to make it a paired bootstrap? e1_2 = e1_sub + e1_e2_com e2_2 = e2_sub + e1_e2_com # create array to hold bootstrap mean differences nbootstraps = 10000 bs_mean_diffs = np.zeros(nbootstraps) # take bootstrap samples many times for ii in range(nbootstraps): # choose which indices will be used from e1_2 and e2_2 inds1 = np.random.randint(0,len(e1)) inds2 = np.random.randint(0,len(e2)) # create your bootstrap samples bs_e1 = e1_2[inds1] bs_e2 = e2_2[inds2] # measure their difference and store it bs_mean_diffs[ii] = bs_e1.mean()- bs_e2.mean() # take the absolute value of…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 =…
- ) Please use the decision table testing method to generate your decision table for Search-A-Node function. b) Please use the decision table testing method to generate your decision table for Add-A-Node function. c) Provide your decision table test case set for (a)d) What is the test complexity when you apply the decision table testing?Write a r programming code for random forest use mtcars dataset and calculate these classification metrics: contusion matrix, accuracy, precision, recall, sensitivity, specificity, F1 score and matthew correlation coefficient.hich of the following statements about Disability adjusted life years (DALYs) and Years of Life Lost (YLLs) due to a specfic disease in a specific location and year, is/are correct? a. To compute the DALYs, you will only need a list of all premature deaths in that location over the year, that were caused by the disease, age at death, and no further information b. To compute the YLLs, you will only need a list of all premature deaths in that location over the year, that were caused by the disease, their age at death, and no further information c. YLLs is expected to be a larger number than DALYs d. To compute YLLs, you will also need to know the period of time (in years) that people were not completely healthy due to the specific disease
- What will be the contents of AX after the following operation?mov ax,63hmov bl,10hdiv blThis HW question needs help5 A. Let * be a new logical connective such that p * q does not hold if and only if p and q are either both false or both true. (i) Write down the truth table for p * q (ii) Write down the truth table for (p * p) * (q * q). (iii) Does the table in (b) coincide with a table in *Truth table available on the given image* (By "Coincide" we mean that the respective columns of T and F values are the same) B. Construct a formula in CNF based on each of the following truth tables: *Truth table available on the given image* C. Compute CNF (NNF ( IMPL_FREE ¬(p → ( ¬ (q ∧ ( ¬p → q))))))
- Need Help with Octave: function [w, t, flag] = margin_pcpt(X, y, max_iter, max_margin)[m, n] = size(X);flag = 0;w = zeros(n, 1);t = 1;while (t < max_iter) && (flag == 0)% Your tasks: % 1. searches for violation point p such that % (a) the label disagrees with p*w; OR % (b) the distance between p and seperation line is too close, i.e. % p*(w./norm(w, 2)) <= max_margin % 2. update the seperation plane vector w % 3. if there is no more violation point, set flag = 1. t = t + 1;end% as a good habit: normalise the seperation vector by it's normw = w./norm(w, 2);endPython! x = np.linspace(0,1,21)y = 2*xnp.random.seed(seed = 1)noise = np.random.normal(0, 0.1, x.size)z = y + noise 1.1Create a plot of these two data sets (?y and ?z) by making two sub-plots with separated markers. Remember to add proper axes labels and titles.\ 1.2 In this question, you should use scipy.optimize.curve_fit function for these two data sets. Please fit linear functions to both of pure data and noisy data, and plot your predicted functions with the original data sets in sub-plots. Remember to add legends.Run a regression predicting bill from temperature, pizzas, and got_wine. Assign the standardized regression coefficients to Q4 by using the lm.beta() function. You should not round these values. The output should look something like this: -0.30580803 0.25976246 0.08161478 Note: CodeGrade will be grading you based on the last line (just the values). Add operator to the regression from Q4. Which is the better model? Assign the better model’s AIC to Q5. Use the classical AIC (k=2). Answe should look like this:9373.33