Which of the following supervised learning methods CANNOT use categorical input variables (i.e. categorical features, predictors)? O multinomial logistic regression naive Bayes classifier neural net random forest all the listed methods can use categorical features Olinear regression
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Q: probabilistic
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A: The machine learning models that can solve classification problems directly are given in step 2. The…
Q: Could you explain your choice of model for machine learning with any examples?
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Q: (a) What is Gradient Descent and Optimizer in Machine Learning?
A: “Since you have asked multiple questions, we will solve the first question for you. If you want any…
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A: EXPLANATION: Following are the four basic types of machine learning algorithms: Supervised machine…
Q: You are given a dataset consisting of images of various types of animals with labels "cat", "dog".…
A: Answer: Option D Multiclass logistic regression
Q: Deep learning models are optimized through 'back propagation' that is built on gradient descent. O…
A: Option: True False
Q: How does Random Forest work? Why is it better than a single decision tree?
A: Since you have asked multiple questions, we will solve the first question for you. If you want any…
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A: Given the value, x1= 0.05 and x2=0.1 w1=0.15 , w2=0.2, w3=0.25, and w4=0.3. b1=0.35 and b2=0.6.…
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A: Below mentioned models are the dimensionality reduction algorithm.
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A: The answer is
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- Explain why you would choose one particular machine learning model over another, giving specific instances. Two distinct grouping strategies exist: K-Nearest Neighbor (a), Going Backwards (c), and Learning More (d)Why we talk about about Predictive, Diagnostic and Prescriptive is also in domain Machine Learning?Justify your preferred machine learning model's use in a given scenario. There are two primary ways to classify items: (A) reminiscing, (C) using K-nearest neighbour, and (D) gaining insight.
- Explain why one machine learning model is better than another. There are two primary types of categorization: This is shown by the methods of k-nearest neighbour (a), retrospective analysis (c), and expanded knowledge (d).Could you please explain your choice of machine learning model using some examples?Explain your machine learning model choice using examples.
- Which machine learning task can be used to directly provide a diagnosis for a disease condition, where the input data is a set of blood test results: Clustering Regression Least squares Regularisation Classification Density Estimation Principal component analysisIn a branch of machine learning known as as is, a model is used to produce a forecast utilising characteristics as inputs and delivering a prediction. Some recent cutting-edge models that have found success include the following:In this section, you will find a brief overview of the process by which the regression models and Artificial Neural Network (ANN) models are created.
- What are some methods to determine the best features to use in a machine-learning model?For unsupervised learning, suggest one way to determine the number of principal components of a dataset which we should use in the system.Use examples to justify your machine learning model choice. Two novel ways to categorise things include the K-nearest neighbour, looking backward, and gathering supplementary information.