Q1. Using Naïve Bayes classifier, classify D6 as "Sports" or "Not sports". (Hint: Use One-addsmoothing to calculate likelihoods.)Documents TextCategorymatch match Olympic game SportsTraining set D1D2election voters votersNot sportsmatch forgettable Olympic|gameforgettable game matchpatries election partiesD3SportsD4SportsNot sportsD5Test setD6parties election voters match ?

Naive Bayes classifier Since Naive Bayes is a probabilistic classifier, we want to calculate the…

Q1. Using Naïve Bayes classifier, classify D6 as "Sports" or "Not sports". (Hint: Use One-add smoothing to calculate likelihoods.) Documents Text Category Training set D1 match match Olympic game Sports election voters voters match forgettable Olympic game forgettable game match patries election parties D2 Not sports D3 Sports D4 Sports D5 Not sports Test set D6 parties election voters match ?

Q1. Using Naïve Bayes classifier, classify D6 as "Sports" or "Not sports". (Hint: Use One-add smoothing to calculate likelihoods.) Documents Text Category Training set D1 match match Olympic game Sports election voters voters match forgettable Olympic game forgettable game match patries election parties D2 Not sports D3 Sports D4 Sports D5 Not sports Test set D6 parties election voters match ?

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Your task is to implement a class that provides methods ProcessTrade, Process PriceUpdate, OutputWorst rade. These method calls correspond to the instructions described above with the method arguments corresponding to the update or query attributes. Constraints • 1 ≤ N, Tradeld, Price, Volume ≤ 105 • It is guaranteed that price update for an instrument is available before first trade on that instrument. Input Format For Custom Testing Input to the program is specified using a simple text format. The format and details of parsing are not relevant to answering the question but custom input can be used to help with development and debugging. The first line of input contains an integer N that denotes the number of instructions. Each of the N subsequent lines contains either an update or a query in the format below: Updates: 4 TRADE . PRICE Query: . WORST TRADE Some example inputs and their expected outputs are described below.
Q51: Which of the following tables will be the better candidate for Incremental Backup? • Customers • Products • Sales • Employees Q52: When normalizing tables and we say that attribute a determines attribute b we mean that : • When we type in a value for a, we can get multiple values for b • When we type in a value for a, we do not get any values for b • When we type in a value for a, we get one and only one value for b • Attribute a is the parent attribute and attribute b is the child attribute
Answer the following questions given the following recurrence relations: 1. T; (n) = 2 x T, (4) + n 2. T, (п) — 4 x Т, + n? 3. T3 (п) —D 8 х T; (" +n 4. TА(п) %3D 8 х Т, (3) + 1 And T, (n) = T2(n) = T3 (n) = T4(n) = 0(1) A. Order the respective time complexities from FASTEST to SLOWEST B. Assuming the same input n, arrange the recurrence relations based on the expected recursion depth from DEEPEST to SHALLOWEST. C. Assuming that each node in their respective recursion trees consume the same amount of constant space, which recurrence relation/s has/have the worst worst-case space complexity (0(M(n)))? Would this/these also require the most amount of space or program stack memory?
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You are working on a spam classification system using regularized logistic regression. "Spam" is a positive class (y = 1)and "not spam" is the negative class (y=0). You have trained your classifier and there are m= 1000 examples in the cross-validation set. The chart of predicted class vs. actual class is: Predicted class: 1 Predicted class: 0 Actual class: 1 85 15 For reference: Accuracy = (true positives + true negatives)/(total examples) Precision = (true positives)/(true positives + false positives) Recall = (true positives)/ (true positives + false negatives) F1 score = (2* precision * recall)/(precision + recall) What is the classifier's F1 score (as a value from 0 to 1)? Write all steps Use the editor to format your answer Actual class: 0 890 10
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Which activation function is appropriate to use in the final layer of a regression ANN? Group of answer choices softmax No activation sigmoind Tanh
Maximum time I'm getting incorrect answer Please give right approach.
We estimate the following LRM for the relationship between COURSE_EVAL and AGE of the instructor: E(COURSE_EVAL | AGE) = 4.774 -0.2154 * log(AGE) where the AGE variable is transformed with the logarithmic function. Provide an interpretation of the coefficient estimate -0.2154.
Consider the training dataset given in Table P3.5. a. Construct a decision tree from the given data using "information gain" based splitting. Table P3.5 D1 D2 D3 D4 D5 D6 D7 D8 D9 Day D10 D11 D12 D13 D14 sunny sunny overcast rain rain Outlook rain overcast sunny sunny rain sunny overcast overcast rain hot hot hot mild cool cool cool mild cool mild mild mild hot mild Temperature high high high high normal normal Humidity normal high normal normal normal high normal high (Outlook sunny, Temperature cool, Humidity high, Wind - strong) ▪ weak Wind strong weak weak weak strong strong weak weak weak strong strong weak strong no no yes yes yes no yes no yes yes yes yes yes no PlayTennis
Train an ID3 decision tree for a dataset shown in the following table. The table contains 2 categorical attributes (refund and marital status) and 1 continuous attribute (taxable income). Once you got the model then use it to classify the input X1 (No, Single, 95K) and X2 (Yes, Divorced, 120K)
1. Identify conditional and join probabilities from the following example. Explain how the probabilities are determined. Example Count, Support, Confidence: Data set D Itemsets T100 134 TID Count(13)=2 T200 235 D = 4 T300 1235 Support(13)=0.5 T400 25 Support(3 →2)=0.5 Confidence(3→2)=0.67