Which line separates y=0 and y=1 in a logistic function?
x=1 is the line which separates the given y=0 and y=1 ion a logistic function. It is because y=0 and y=1 makes the perpendicular line and x=1 is the horizontal line which cuts the perpendicular line and separates the line. The logistic function is a “S” shaped curve or sigmoid curve.
Is there a machine learning algorithm for logistic regression?
There are many machine learning algorithms. We have presented logistic regression in supervised learning in this article. Machine learning programming languages are designed with pre-built libraries and advanced support of data science and data models. We have shown examples to implement logistic regression using MATLAB.
What is machine learning and how does it work?
Machine learning is a branch of artificial intelligence and computer science that focuses on using data and algorithms to imitate the way humans acquire knowledge and gradually improve its accuracy.
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Is the line that separates y 0 and y 1 in a logistic function 1 marks ans decision boundary None of the options divider seperator?
x=1 is the line that separates y = 0 and y = 1 in a logistic function.Nov 8, 2018
What is the range of the output values for a sigmoid function?
between 0 and 1That is, the input to the sigmoid is a value between −∞ and + ∞, while its output can only be between 0 and 1.
What is the output of sigmoid function when the input is 0?
Sigmoid: It is also called as a Binary classifier or Logistic Activation function because function always pick value either 0(False) or 1 (True). The sigmoid function produces similar results to step function in that the output is between 0 and 1.Jan 3, 2022
What is the range of the output values for a sigmoid function 0 INF?
Sigmoid functions most often show a return value (y axis) in the range 0 to 1. Another commonly used range is from −1 to 1.
Answer
A logistic function or logistic curve is a common "S" shape (sigmoid curve), with equation: where. e = the natural logarithm base (also known as Euler's number), x0 = the x-value of the sigmoid's midpoint, L = the curve's maximum value,
Answer
The decision boundary or threshold is the line that separates the area where y = 0 and where y = 1
New questions in Computer Science
Which kind of image is indispensable and needs added text to go with it? A. a map B. a chart C. a graph D. a photograph
Logistic Regression in Machine Learning
Machine learning is a branch of artificial intelligence and computer science that focuses on using data and algorithms to imitate the way humans acquire knowledge and gradually improve its accuracy.
Classification Algorithms
Classification algorithms assort test data into specific categories. There are many use cases in real life, such as animal recognition between cats and dogs, weather forecast for rain or shine, artifacts identification as authentic or fake.
Logistic Regression Theory
Logistic regression is used for predicting the categorical dependent variable ( y) using a given set of independent variables ( x ). It is one of the most popular Machine Learning algorithms.
Logistic Regression Programming
From logistic regression theory, we can see the computation algorithm involves advanced mathematics. It is not very easy if we construct the model by ourselves.
Conclusion
There are many machine learning algorithms. We have presented logistic regression in supervised learning in this article. Machine learning programming languages are designed with pre-built libraries and advanced support of data science and data models. We have shown examples to implement logistic regression using MATLAB.
Appendix
Some MATLAB programs are used to produce images for this article, and we would like to share them here.
Logistic Function
Logistic regression is named for the function used at the core of the method, the logistic function.
Representation Used for Logistic Regression
Logistic regression uses an equation as the representation, very much like linear regression.
Logistic Regression Predicts Probabilities (Technical Interlude)
Logistic regression models the probability of the default class (e.g. the first class).
Learning the Logistic Regression Model
The coefficients (Beta values b) of the logistic regression algorithm must be estimated from your training data. This is done using maximum-likelihood estimation.
Making Predictions with Logistic Regression
Making predictions with a logistic regression model is as simple as plugging in numbers into the logistic regression equation and calculating a result.
Prepare Data for Logistic Regression
The assumptions made by logistic regression about the distribution and relationships in your data are much the same as the assumptions made in linear regression.
Further Reading
There is a lot of material available on logistic regression. It is a favorite in may disciplines such as life sciences and economics.
