Knowee
Questions
Features
Study Tools

Q.No.5. The _________ learning model predicts a continuous target variable as a linear functionof input features.

Question

Q.No.5.

The _________ learning model predicts a continuous target variable as a linear function of input features.

🧐 Not the exact question you are looking for?Go ask a question

Solution

The answer is "linear regression" learning model predicts a continuous target variable as a linear function of input features.

In linear regression, the relationship between the input variables (features) and the output variable (target) is modeled as a linear equation. It assumes that changes in the input variables will produce proportional changes in the output. Linear regression is commonly used in various fields, including economics, biology, and social sciences, for predicting outcomes based on different predictor variables. The model can be represented mathematically as:

y=β0+β1x1+β2x2++βnxn+ϵ y = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \ldots + \beta_n x_n + \epsilon

where yy is the predicted outcome, x1,x2,,xnx_1, x_2, \ldots, x_n are the input features, β0\beta_0 is the y-intercept, β1,,βn\beta_1, \ldots, \beta_n are the coefficients for each feature, and ϵ\epsilon is the error term.

This problem has been solved

Similar Questions

Question 7All of the listed below are part of the Machine Learning Framework, except:1 pointObservationsFeaturesParametersNone of the above

Select the correct statement(s) about using Linear Regression for classification:

1. How does continuous learning contribute to success?It doesn'tDelays successEnhances adaptabilityLimits potential

On what parameters can change in the weight vector depend?Question 31Answera. Input vector.b.Learning signalc.Learning parametersd.All

on each step it visits a location of a data point and predicts the value at that location by leaving out the observed value;Question 6Answera.Yesb.No

1/1

Upgrade your grade with Knowee

Get personalized homework help. Review tough concepts in more detail, or go deeper into your topic by exploring other relevant questions.