Which of the following classifiers is least likely to underfit or overfit the data? Group of answer choices a) Linear b) KNN c) NN
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Which of the following classifiers is least likely to underfit or overfit the data?
Group of answer choices
a) Linear
b) KNN
c) NN
Answer with proper explanation else skip
Step by step
Solved in 2 steps
- this answer hasnt cover all the questiion. Would you cover the all part of the question?Question 8 Find the score (mean accuracy) of your knn classifier using x_test and y_test. This function should return a float between 0 and 1 [ ]: def answer_eight(): # YOUR CODE HERE raise NotImplementedError() [ ]:help plz i will rate you discrete structure
- True or false: In RapidMiner, the label (dependent variable) can be coded either alphabetically (e.g., true/false) or numerically (e.g., 0/1). True or false: The Logistic Regression operator in RapidMiner offers only one algorithm for model creation.Help, please in C# programming languageA database of spontaneous adverse reaction reports contains a total of 2 million reports. Among them, there are 80,000 (4%) dealing with CNS side effects (for all drugs) and a total of 40,000 (2%) dealing with painkillers (all reactions). There are a total of 1,600 reports dealing with CNS side effects as a side effect of painkillers. Which of the following statements is CORRECT? A) The over-frequency described is not significant and thus one can not conclude one by one about the relationship between painkillers and CNS side effects. B) Proportional reporting ratio is 5, ie. it is a signal. C) Proportional reporting ratio is 0.50, which is not high enough to raise suspicion of a signal. D) Proportional reporting ratio is 1 and thus it is not a signal. E) Odds ratio for the association is 2, ie there is strong knowledge of a causal relationship between the use of painkillers and risk of CNS side effects.
- ASSIGNMENTS: Question 1: Given: enum cropType {WHEAT, CORN, RYE, BARLEY, OATS}: сгoрТуре crop: Circle the correct answer. a. static_cast(WHEAT) is 0 (i) true (ii) false b. static_cast(static_cast(WHEAT)- 1) is WHEAT (i) true (ii) false c. RYE > WHEAT (i) true (ii) false d. for (crop = wheat; crop <= oats; ++crop) cout << "*". cout << endl; outputs: (i) true (ii) false. *****1. An enumeration type is a set of ordered values. True FalseFollow these steps:● Read the example file.● Try to think of a relationship you can model and create a new Python filein this folder called poly.py.● Inside poly.py, identify a relationship, and use Polynomial regression totrain, predict, and plot your results.
- Use the following confusion matrices to answer Questions 4 – 13. Please note that some of the questions may have a single correct answer, while some questions may have multiple correct answers. A Classifier Predicted class True class No Yes No 75 75 Yes 75 75 B Classifier Prediction True No Yes No 0 50 Yes 0 250 C Classifier Prediction True No Yes No 0 149 Yes 0 151 D Classifier Prediction True No Yes No 25 25 Yes 125 125 E Classifier Prediction True No Yes No 149 0 Yes 151 0 F Classifier Prediction True No Yes No 50 0 Yes 200 50 G Classifier Prediction True No Yes No 10 40 Yes 10 240 H Classifier Prediction True No Yes No 30 20 Yes 35 215 1 - Which classifier is a random classifier? (Multiple Answers) Group of answer choices A B C D E F G H 2 - Which classifier is a majority-rule classifier? (Multiple Answers) Group of answer choices A B C D E F G H 3 - Which classifier…Create a question, a question should be covering the independent variable. Please help. This is for the 'STATEMENT OF THE PROBLEM'def classify(classifier, point): """ return the classification probability for the given point using the given classifier :params: classifier: list of floats, representing the weights for a logistic classifier point: list of values, representing features of a data point. will be same length as list of weights return: float, representing probability that point is from class 1. """ # TODO: implement this! # placeholder; say "50-50" for any point return 0.5
