Linear regression Vs Logistic regression in research: #data #datascience #biostatistics #machinelearning #foryou #viral
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‎Ayoub Abounamer | أيوب أبونمر ‍♂️‎ on Instagram: "First two algorithms to know when you start machine learning are Linear regression and logistic regression , libaghi nsift lih ressources ikhli “algo” f comment 欄 . . . . #data #datascience #ai #fyp #python #algo #algorithms #machinelearning #machinelearning"
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Insightforge | Data Science & AI on Instagram: "Why do neural networks crush linear models on complex data? If your dataset has 2 classes but isn’t linearly separable, models like logistic regression or a linear SVM will struggle - they can only draw a straight-line boundary. Neural networks, on the other hand, unlock nonlinearity through activation functions like ReLU, sigmoid, or tanh. Each layer applies these functions to reshape the input space, layer by layer, into richer and more flexible
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babal chodi ka on Instagram: "Logistic Regression is a statistical and machine learning technique used for binary classification problems, where the target variable has two possible outcomes (e.g., fraud/not fraud, spam/not spam, default/no default). Despite its name, logistic regression is actually a classification algorithm, not a regression one. --- 1. What is Logistic Regression? Logistic regression is used to model the probability that a given input belongs to a particular category. It is b
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Artificial Intelligence | AI on Instagram: "Logistic regression is a statistical method used for binary classification problems, where the goal is to predict one of two possible outcomes. It uses the logistic function (also known as the sigmoid function) to map predicted values to probabilities between 0 and 1. If the probability is above a certain threshold, the input is classified into one category; otherwise, it is classified into the other. C: 3 minute data science (yt)"
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