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Mashinali o'qitish uchun chiziqli algebra. Chiziqli algebra masalalarini dasturlash
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| bet | 4/8 | | Sana | 13.05.2024 | | Hajmi | 0,64 Mb. | | #230369 |
Bog'liq Mashinali o\'qitish uchun chiziqli algebra. Chiziqli algebra masaFaollashtirish funktsiyasi (faollashtirish funktsiyasi)(\(\phi(net) \) ) argument sifatida vaznli yigʻindini oladigan funksiyadir. Ushbu funktsiyaning qiymati neyronning chiqishi (\(out \) ).
Sun'iy neyronlarning umumiy sonini o'zgartirish ikki sababga ko'ra ma'nosizdir. Birinchidan, umuman olganda hisoblash elementlari sonining ko'payishi tizimni faqat og'irroq va ortiqcha qiladi. Ikkinchidan, agar siz 100 o'rniga 1000 ahmoq yig'sangiz, ular hali ham savolga to'g'ri javob bera olmaydilar.
Mashinali o‘qitishda sinflashtirish algoritmlari va ularni dasturlash.
Classification is a natural language processing task that depends on machine learning algorithms.
There are many different types of classification tasks that you can perform, the most popular being sentiment analysis. Each task often requires a different algorithm because each one is used to solve a specific problem.
Computer Scientist David Wolpert explains in his paper, The Lack of A Priori Distinctions Between Learning Algorithms. Neural Computation, that:
"For each problem, you must select the right algorithm. Your question is how to do this. If you have plenty of computational resources, you can test multiple algorithms and parameter settings. In this approach, the main question is how to estimate and compare the performance of the algorithms in a reliable way."
Before introducing you to the different types of classification algorithms to choose from, let’s quickly go over what classification is.
What Is Classification?
Classification is the process of recognizing, understanding, and grouping ideas and objects into preset categories or “sub-populations.” Using pre-categorized training datasets, machine learning programs use a variety of algorithms to classify future datasets into categories.
Classification algorithms in machine learning use input training data to predict the likelihood that subsequent data will fall into one of the predetermined categories. One of the most common uses of classification is filtering emails into “spam” or “non-spam.”
In short, classification is a form of “pattern recognition,” with classification algorithms applied to the training data to find the same pattern (similar words or sentiments, number sequences, etc.) in future sets of data.
Using classification algorithms, which we’ll go into more detail about below, text analysis software can perform tasks like aspect-based sentiment analysis to categorize unstructured text by topic and polarity of opinion (positive, negative, neutral, and beyond).
Try out this pre-trained sentiment classifier to understand how classification algorithms work in practice, then read on to learn more about different types of classification algorithms.
Top 5 Classification Algorithms in Machine Learning
The study of classification in statistics is vast, and there are several types of classification algorithms you can use depending on the dataset you’re working with. Below are five of the most common algorithms in machine learning.
Popular Classification Algorithms:
Logistic Regression
Naive Bayes
K-Nearest Neighbors
Decision Tree
Support Vector Machines
Logistic Regression
Logistic regression is a calculation used to predict a binary outcome: either something happens, or does not. This can be exhibited as Yes/No, Pass/Fail, Alive/Dead, etc.
Independent variables are analyzed to determine the binary outcome with the results falling into one of two categories. The independent variables can be categorical or numeric, but the dependent variable is always categorical. Written like this:
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