You are creating a model to predict housing prices. Due to budget constraints, you must run it on a single resource-constrained virtual machine. Which learning algorithm should you use?
Correct answer is A. A tip here to decide when a liner regression should be used or logistics regression needs to be used. If you are forecasting that is the values in the column that you are predicting is numeric, it is always liner regression. If you are classifying, that is buy or no buy, yes or no, you will be using logistics regression.
Liner Regression is correct but this is one aspect of the question, how does it relates to resource constrained machines? or that could be just a distraction?
the keyword here is running it on a single resource-constrained virtual machine. linear regression is a simple and efficient algorithm that is well-suited for predicting continuous values.
Linear regression is a simple and resource-efficient algorithm for predicting continuous values like housing prices. It's computationally lightweight and well-suited for single machines with limited resources. It doesn't require the extensive computational power or specialized hardware that more complex algorithms like neural networks (options C and D) might need.
Option B (Logistic classification) is used for binary classification tasks, not for predicting continuous values like housing prices, so it's not the right choice in this context.
Here, due to budget constraints, we're utilizing a single resource-constrained virtual machine, operating in a minimal resource environment. Linear regression emerges as the appropriate algorithm. It's a lightweight predictive model that suits our resource limitations
Linear regression will be used since the prediction requires forecasting prices involving numeric values and is computationally less resource intensive
Correct Answer is A. Since linear regression is used to predict a numeric value. While logistic regression is used to classify among the binary scenario.
Further option C and D are advance ML options and not cost and resource effective for the current situation.
correct answer -> Linear Regression
Linear regression is a statistical method that allows to summarize and study relationships between two continuous (quantitative) variables: One variable, denoted X, is regarded as the independent variable. The other variable denoted y is regarded as the dependent variable. Linear regression uses one independent variable X to explain or predict the outcome of the dependent variable y.
Whenever you are told to predict some future value of a process which is currently running, you can go with a regression algorithm.
Linear regression is a simple and computationally efficient algorithm that can be used to predict a continuous target variable based on one or more input variables. It is particularly well-suited for resource-constrained environments, as it requires minimal computational resources and can be run on a single virtual machine.
Linear regression is a good fit for this problem as it is a supervised learning algorithm that can be used for regression problems, and it's not computationally expensive.
Option B is not recommended as Logistic classification is a supervised learning algorithm that is used for classification problems, not regression problems.
Option C and D are not recommended as Recurrent Neural Network (RNN) and Feedforward Neural Network (FNN) are computationally expensive and may require significant computational resources and memory to run on a single virtual machine.
A as Supervised learning using Regression can help build a model to predict house prices.
Option B is wrong as Classification would not help to solve the problem.
Options C & D are wrong as they would need more resources.
Ok the right answer is A, but the question is why? Then:
- B not because we are make forecasting and not classifying
- C and D not because this solution need more nodes, then more VM.
Right?
A voting comment increases the vote count for the chosen answer by one.
Upvoting a comment with a selected answer will also increase the vote count towards that answer by one.
So if you see a comment that you already agree with, you can upvote it instead of posting a new comment.
Radhika7983
Highly Voted 4 years agoAnirkent
3 years, 10 months agomuzammilnxs
3 years, 9 months ago[Removed]
Highly Voted 4 years, 8 months agoSamuelTsch
Most Recent 1 month agortcpost
2 months agoAshishDhamu
9 months, 1 week agoFazan456
10 months, 2 weeks agoRT_G
1 year agorocky48
1 year agoAmmarFasih
1 year, 6 months agoZosby
1 year, 9 months agoJJJJim
1 year, 9 months agolukas_xls
1 year, 11 months agorowan_
2 years, 3 months agosamdhimal
2 years, 10 months agosamdhimal
1 year, 10 months agoMaxNRG
3 years agoanji007
3 years, 1 month agoStefanoG
3 years, 2 months ago