Answered: Task 1: For the training set given… | bartleby

Related questions

Question

Task 1:1
For the training set given below, predict the classification of the
following sample X = (2,1,1, Class = ?) - Calculate every step
using Simple Bayesian Classifier
Using libraries
Sample
Class
C
1234567
2
3
4
5
Attributel
A1
1
021021
Attribute2
A2
and not us
Attribute3
A3
2012120
1121221
1
212NN--
2
2

Expert Solution

Trending now

This is a popular solution!

Step by step

Solved in 7 steps with 6 images

SEE SOLUTION Check out a sample Q&A here

Blurred answer

Similar questions

We use the Breast Cancer Wisconsin dataset from UCI machine learning repository: http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29 Data File: breast-cancer-wisconsin.data (class: 2 for benign, 4 for malignant) Data Metafile: breast-cancer-wisconsin.names Please implement this algorithm for logistic regression (i.e., to minimize the cross-entropy loss as discussed in class), and run it over the Breast Cancer Wisconsin dataset. Please randomly sample 80% of the training instances to train a classifier and then testing it on the remaining 20%. Ten such random data splits should be performed and the average over these 10 trials is used to estimate the generalization performance. You are expected to do the implementation all by yourself so you will gain a better understanding of the method. Please submit: (1) your source code (or Jupyter notebook file) that TA should be able to (compile and) run, and the…
7. group_friends_by_food For this problem, you are given a mostly-working version of the Friend and FriendsDB classes from hw6 and hw7, and we will add new method to FriendsDB -- group_friends_by_food. group_friends_by_food returns a dictionary mapping from each of the favorite foods enjoyed by any friend to a list of the friends who enjoy that food, sorted in alphabetical order. (You can use the friends_who_love method to generate these lists.) A sample run should look like this: |>>> friend1 = Friend ("sarah", 165) >>> friend1.add_favorite_food ("strawberries") >>> friend2 - Friend ("dweezil", 175) >>> friend2.add_favorite_food("pizza") >>> friend3 = Friend("bimmy", 60) >>> friend3.add_favorite_food("pizza") >>> friend3.add_favorite_food("strawberries") >>> db = FriendsDB() >>> db.add friend (friend1) >>> db.add_friend(friend2) >>> db.add friend(friend3) >>> db.group_friends_by_food() {'strawberries': ['bimmy', 'sarah'], 'pizza': [' bimmy', 'dweezil']}
Respond to the question with a concise and accurate answer, along with a clear explanation and step-by-step solution, or risk receiving a downvote.
Data Mining The following is the Training Data which is the result of an average monitoring for 2 weeks of 8 people who are suspected of being infected with the Omicron variant of the Corona virus. Based on the data, make a Decision Tree, using the attribute selection measures "GINI Index". With the class attribute is the column "Infected"
Use decision tree to further explore the dataset, where the dependent variable is ‘smoker’. Please explain the approach taken. [No more than 300 words]
Please implement Multinomial Logistic Regression on the following data. Please continue from the given code:
7. group_friends_by_food For this problem, you are given a mostly-working version of the Friend and FriendsDB classes from hw6 and hw7, and we will add new method to FriendsDB -- group_friends_by_food. group_friends_by_food returns a dictionary mapping from each of the favorite foods enjoyed by any friend to a list of the friends who enjoy that food, sorted in alphabetical order. (You can use the friends_who_love method to generate these lists.) A sample run should look like this: >>> friend1 = Friend("sarah", 165) >>> friend1.add favorite_food("strawberries") >>> friend2 Friend("dweezil", 175) >>> friend2.add_favorite_food("pizza") >>> friend3 Friend("bimmy", 60) >>> friend3.add_favorite_food("pizza") >>> friend3.add favorite_food ("strawberries") >>> db FriendsDB() %3D >>> db.add_ friend(friend1) >>> db.add_friend(friend2) >>> b.add_friend (friend3) >>> db.group_friends_by_food() {'strawberries': ['bimmy', 'sarah'], 'pizza': ['bimmy', 'dweezil']}
The following dataset is a historic record of 14 houses that were sold in a small town in BC. The dataset is used to predict whether a new house in the same town will be sold in 10 days if listed with a specific price based on certain attributes. We are considering only four attributes (price, number of bedrooms, size, and distance to bus stop) just to simplify the calculations in this assignment but more attributes should be considered in real applications. Build a decision tree to predict whether a new house listing in the same town will be sold in 10 days based on the given attributes. Use ID3 algorithm. To answer this question, you need to complete the following steps: Calculate the entropy of the whole dataset After identifying the first attribute, repeat the same steps to identify the next attribute to split on in every leaf of the tree based on information gain analysis. Repeat this step until you complete the tree. Draw the final tree