• Example: Following set of training data Instance Classification a₁ 1 T 2 T 3 T 4 F F 5 F 6 FT ++1+1 TTFFTT What is the entropy? What are the information gains? Draw the decision tree. What is the disjunctive rule?

Q: In predictive modelling, what is a Tuning Parameter? In the Random Forest and Boosting Model, what…

A: Forecasting future behavior using predictive modeling is a widely used statistical technique. In…

Q: In machine learning we learn from data. The data can have many instances and features (or…

A: Features are specific independent variables that function as a system input in machine learning.…

Q: it resulted in the creation of the

A: Boehm's Spiral Model is notable because:

Q: What do you mean by "inference rules"? With an example, explain "ahead chaining" and "reverse…

A: The templates for creating valid arguments are known as inference rules. In artificial intelligence,…

Q: What is attribute inheritance? Why is it in1portant?

A: Given: What is attribute inheritance? Why is it in1portant?

Q: probabilistic

A: Data matching is an important element of data analysis. It involves comparing multiple datasets in…

Q: What is a Tuning Parameter in predictive modeling? What tuning parameters have been separately…

A: TUNING PARAMETER IN PREDICTIVE MODELING: The first parameter in the predictive is the total number…

Q: Discuss data imbalance in a binary classification task (i.e., output is either True or False)

A: Let's take the example of Fraud detection. Fraud detection is a very common problem that people try…

Q: Manually train a decision tree based on the following dataset.  a) Please show the detailed process…

A:

Q: While the classes are clearly ordered, we may rank some from biggest to smallest, making this…

A: Data similarity: While the classes are clearly organised, we may rank some from biggest to smallest…

Q: The data mining technique involved in predicting a categorical response is called as. A. Regression…

A: The data mining technique involved in predicting a categorical response is called as - B)…

Q: Create a decomposition diagram in for a Medical Store ?

A: A decomposition diagram is a representation of the functional architecture of an entire system along…

Q: if you are running 24 design with a single replicate experiment, and you need to block a nuisance…

A: 24 runs needed.

Q: Decision Tree / We want to learn a function that returns (counts) the number of attributes with the…

A: The decision tree representing the above function and explanation of the above function are as…

Q: 3. For a given data set, we applied the 0.632 bootstrapping validation and built a decision tree…

A: The overall error estimate for the model is 0.216, which is a weighted average of the training error…

Q: f we add more independent variables into the model:   A. The adjusted R2 value will increase.   B.…

A: Answer :

Q: The relations from Exercise 3 are:  a. (1,1), (1,3), (2,2), (3,1), (3,3) b. (1,2), (2,2), (3,2)…

A: - We need to create directed graphs from the list provided.

Q: We use AdaBoost to perform classification on 5 objects

A: We are given a total of 5 objects for classification. That means our sample weights are (1/5, 1/5,…

Q: Suppose we train a model to predict whether an email is Spam or Not Spam. John always forgets to…

A: The above question is solved in step 2 :-

Q: What evaluation strategies for a classification problem (e.g., random subsampling, holdout, k-fold…

A: When dealing with imbalanced classification problems, where the classes are not represented equally…

Q: A 'model' refers to to following: Select one: a. A model is the method that can solve the machine…

A: Your answer is given below with an explanation.

Q: Explain the example of concatenation using Matlab. Please attach the simulation also. Use the method…

A: We can easily do concatenation using matrix the code for horizontal concatenation is given as below:…

Q: Assume there are three hypotheses, h1, h2, h3, which are trained from the same data set D. The…

A: To find the predicted result of the Bayes optimal classifier, we need to calculate the posterior…

Q: AuctionBase In this project, we are developing an online e-commerce website (similar to eBay). You…

A: the solution is an given below :

Q: Is it possible to explain the distinction between descriptive and inferential statistics?

A: Introduction: The properties of a data collection are summarized using descriptive statistics.…

Q: Write down step-by-step procedure for model estimation in NLOGIT software package?

A: Extension of LIMited DEPendent Variable Models (LIMDEP)Nested LOGIT Models (NLOGIT) – Descriptive…

Q: - The overall entropy is (round to 2 decimals): - The information gain for attribute A is (round to…

A: The purpose of machine learning models and Data Scientists in general is to minimize uncertainty,…

Q: REPLY TO THIS POST,,,, A comparative system that measures behaviors compares employee reviews to…

A: Answers:- While ranking systems such as simple rank order, alternation rank, paired comparison,…

Q: Consider data with two Boolean attributes A and B. A B Label count of examples 50 1 50 1 1 100 What…

A: The contingency table after splitting on attributes A and B are :   A=1 A=0 + 1 0 - 1 2…

Q: In decision trees, attribute selection algorithms evaluate whether a nominal attribute is great or…

A: Introduction: The term "attribute selection technique" refers to approaches for picking a subset of…

Q: Develop a CART model using the test data set that utilizes the same target and predictor variables.…

A: Cart Model: Essentials of Decision Tree:- The decision tree method is a powerful and popular method…

Q: When you start training a new model, you can O start with a high learning rate, so the model could…

A: Ans: When you start training a new model , you can  b) Train the model using the entire dataset with…

Q: For the following data set. Use Decision Tree classification to build the model to classify the test…

A: The decision tree classification to bulid the model to classify the test records is as follows.

Q: Explain the decision tree. Illustrate the concept of a decision tree by taking a suitable example…

A: Decision Tree: A decision tree is a very specific type of probability tree that enables you to make…

Q: A nominal attribute's quality is determined by the purity of its relevant partitions in decision…

A: TECHNIQUES FOR ATTRIBUTE SELECTION: The term "attribute selection technique" refers to the methods…

Q: Task 1: For the training set given below, predict the classification of the following sample X =…

A: Simple Naive bayes classifier I will consider to classify the given data.   Naive bayes classifier :…

Q: During the model training using a neural network, the performance log obtained resembles the…

A: Option A: Model indicate under-fitting

Q: You want to build a text classifier that takes in a sequence of text and outputs its sentiment. You…

A: Introduction BERT: Google created the BERT (Bidirectional Encoder Representations from Transformers)…

Q: For the following confusion matrix, what is the weighted accuracy if the weight for the Cat class is…

A: Task : Given the confusion matrix. The task is to find the weighted accuracy from the given matrix.

Q: In Decision Trees, how do I find how many: • attributes • classes • instances Are in a dataset?…

A: It is a type of machine learning algorithm that are used for classification and regression.The…

Q: A group of international experts in fuzzy set theory were invited to classify the overall…

A: Approach to solving the question: Here's a Python-like pseudocode to illustrate the steps:import…

Q: Please provide steps to answer this question: Your task is simple: use machine learning to create a…

A: Given,  What you need to do: 1. Data cleansing 2. Split the dataset (i.e., train.csv) into a…

Q: have a 70000x785 dataset (70000 instances and 785 features) consisting of 10 different types of…

A: Here as given,10 different classes , in which we have 7000 instances and 785 features.…