Machine Learning - Supervised Learning - Actual Values Related To The Variable That Needs To Be Predicted Are Available

Giriş

Bazı notlarım şöyle. İlk bölüm için Machine Learning yazına bakabilirsiniz.

Supervised Learning Nedir?

Açıklaması şöyle. Bu yöntemde elimizdeki girdiye karşılık, bazı cevaplar (label deniyor) vardır. Bilgisayar bu ikisini kullanarak, daha önce görülmemiş bir veriye göre tahminde bulunur

Supervised Learning takes advantage of already known labels, like whether an email is reported spam or not, how much rainfall has occurred in the last 7 days, whether a lump in body is carcinogenic or not etc.

Açıklaması şöyle

Supervised Learning takes advantage of already known labels, like whether an email is reported spam or not, how much rainfall has occurred in the last 7 days, whether a lump in body is carcinogenic or not etc. Supervised learning is a high level categorization of ML problems which defines all challenges where we have at least some solved/labeled data. This is opposed to unsupervised learning (we don't know the solution) and reinforcement learning (data and labels are generated procedurally).

Algoritmalar Nelerdir

Çok kullanılan bazı algoritmalar şöyle

Naive Bayes Classifier

Support Vector Machine

Decision Tree

Random Forest

Regression

Classification

Regression ve Classification Farkı Nedir?

Açıklaması şöyle

Regression (Predict the numerical value given the data set)

Classification (Predict the class or the label of the dataset)

1.1 Decision tree

Örnek

Açıklaması şöyle

... below is a diagram representing a decision tree algorithm predicting if a passenger survived the Titanic. By looking at the figure, we see that the model is picking up on the "women and children first" policy that was enforced during the tragedy.

Şeklen şöyle

Random forests

Açıklaması şöyle

Decision trees by themselves generally don't perform very well, which is why there are ensemble methods such as random forests. These algorithms use many trees, each of which makes a prediction. They then all "vote" to get a final prediction.

1.2 Support Vector Machines (SVM) Yöntemi

Machine - Learning - Support Vector Machines yazısına taşıdım

1.2 Regression Yöntemi

Açıklaması şöyle

Finally regression is a specific mathematical algorithm which can help us achieve tasks and might be opposed to algorithms such as a Neural Net, Naive Bayes, etc.

1.4 Classification Yöntemi
Açıklaması şöyle

Machine learning classification is a type of supervised learning in which an algorithm maps a set of inputs to discrete output. 

Classification için sadece Machine Learning kullanmak şart değil. Açıklaması şöyle

Machine/statistical learning is one approach to classification, but not the only one. Simple rules created by humans are probably more common in computer programs than ones created by ML.

Benzer bir açıklama şöyle

Actually, classification methodology has been around in classical probability and statistics for the better part of a century, well before "machine learning" was a deal. ... Machine learning algorithms are simply attempts to approximate this historically well-known (at least by statisticians) optimal solution.