Machine Learning Fundamentals – Linear & Logistic Regression

Linear Regression is a supervised regression-based machine learning algorithm. Based on independent variables, regression models a target prediction value. It is primarily used to determine the relationship between variables and forecasting. Different regression models differ in the type of relationship they consider between the dependent and independent variables, as well as the number of independent variables used.
Logistic regression is a supervised classification algorithm. The output (or target variable) y in a classification problem has a finite number of possible categories (features) (or inputs) (X).

KEY FEATURE

Machine Learning is a type of AI that allows a computer or machine to make predictions based on its analysis of historical data.

The key study by Alan Turing established a criterion, which is that a machine must be intelligent and responsive in a way that cannot be distinguished from that of a human.

With the help of artificial intelligence, computers can “learn” from their past mistakes and successes, and then use that knowledge to predict the future.

Profitability, reduced costs, optimised production, environmental consciousness, and superior customer service are just some of the advantages machine learning offers to businesses.

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Key Topics

    Linear Regression Features

    • A supervised regression model is linear regression.
    • Linear Regression predicts the value using an integer number.
    • There is no activation function used here.
    • There is no need for a threshold value in this case.
    • To predict the next weight value, we compute Root Mean Square Error (RMSE).
    • In this case, the dependent variable should be numerical, and the response variable should be continuous to value.
    • It is calculated using the least squares method.
    • When we plot the training datasets, we can draw a straight line that touches the most plots.
    • Linear regression is used to estimate the dependent variable when the independent variables change. Predict the price of a house, for example.
    • Linear regression assumes that the dependent variable has a normal or gaussian distribution.

    Logistics Regression Features

    • A supervised classification model is logistic regression.
    • We predict the value by 1 or 0 in Logistic Regression.
    • In this case, the activation function is used to transform a linear regression equation into a logistic regression equation.
    • A threshold value is added here.
    • In this case, precision is used to forecast the next weight value.
    • The dependent variable in this case has only two categories. Given a set of quantitative or categorical independent variables, logistic regression estimates the odds outcome of the dependent variable.
    • It employs maximum likelihood estimation.
    • Any change in the coefficient changes the direction and steepness of the logistic function. Positive slopes produce an S-shaped curve, while negative slopes produce a Z-shaped curve.
    • In contrast, logistic regression is used to calculate the likelihood of an event. For example, determine whether the tissue is benign or malignant.
    • The dependent variable is assumed to have a binomial distribution in logistic regression.
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    • There are numerous names for linear regression.
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    Linear regression does not require any knowledge of statistics or linear algebra. This is a gentle high-level introduction to the technique that will provide you with enough background to apply it effectively to your own problems.

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