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Advantages and Disadvantages of Logistic Regression

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Logistic Regression is one of the supervised Machine Learning algorithms used for classification i.e. to predict discrete valued outcome. It is a statistical approach that is used to predict the outcome of a dependent variable based on observations given in the training set. Logistic Regression is one of the simplest machine learning algorithms and is easy to implement yet provides great training efficiency in some cases. Also due to these reasons, training a model with this algorithm doesn't require high computation power. The predicted parameters (trained weights) give inference about the importance of each feature.



Building A Logistic Regression model in Python - Nucleusbox

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Welcome to another blog on Logistic regression in python. In previous blog Logistic Regression for Machine Learning using Python, we saw univariate logistics regression. And we saw basic concepts on Binary classification, Sigmoid Curve, Likelihood function, and Odds and log odds. In, this section first will take a look at Multivariate Logistic regression concepts. And then we will be building a logistic regression in python.


Supervised Learning with Azure

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Several steps need to be performed during the preparation phase to transform images/sounds into numerical vectors accepted by the algorithms. Regression on text data: Training data consists of texts whose numerical scores are already known. Several steps need to be performed during the preparation phase to transform the text into numerical vectors accepted by the algorithms. Examples: Housing prices, Customer churn, Customer Lifetime Value, Forecasting (time series), and Anomaly Detection.


Logistic Regression Vs Decision Trees Vs SVM: Part I - Edvancer Eduventures

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Classification is one of the major problems that we solve while working on standard business problems across industries. In this article we'll be discussing the major three of the many techniques used for the same, Logistic Regression, Decision Trees and Support Vector Machines [SVM]. All of the above listed algorithms are used in classification [ SVM and Decision Trees are also used for regression, but we are not discussing that today!]. Time and again I have seen people asking which one to choose for their particular problem. Classical and the most correct but least satisfying response to that question is "it depends!".