Supervised Learning and Classification

12 Questions

الهدف من التعلم تحت الإشراف هو?

تعلم الخريطة بين بيانات الإدخال وال_etiquettes_ الخروج

ما هو 이름 من مفاهيم في مشكلة التعلم تحت الإشراف التي يحويلي البيانات إلى فئات مختلفة؟

التصنيف

ما هو الاختلاف الرئيسي بين الشجرة القرارية وغابة عشوائية؟

مستوى التعقيد

ما هو اسم الخوارزمية التي تستخدم دالة لوغستية لنمذجة احتمال انتماء الفئة؟

اللوغستية الانحدار

ما هو الغرض من دالة الحد الأقصى في خوارزمية SVM؟

تخصيص الحدود الأقصى

ما هو نوع من مشكلة التعلم تحت الإشراف التي تحويلي البيانات إلى قيم عددية؟

التنبؤ بالقيمة

What is the primary goal of supervised learning?

To learn a mapping between input data and the corresponding output labels

Which type of classification problem involves predicting multiple labels for an instance?

Multi-Label Classification

What is the term for when a model is too complex and performs well on the training data but poorly on the testing data?

Overfitting

What is the primary difference between classification and regression in supervised learning?

The type of output variable predicted

What is the purpose of the testing data in supervised learning?

To evaluate the model's performance

What is the type of supervised learning problem that involves predicting a continuous value or range?

Regression

Study Notes

Supervised Learning

Type of machine learning where the algorithm learns from labeled data
Goal: learn a mapping between input data and output labels
Training data consists of input-output pairs $(x, y)$
Algorithm learns to predict output $y$ for new, unseen input $x$

Classification

Type of supervised learning problem where the output is categorical
Goal: predict a class label or category that an instance belongs to
Examples:
- Spam vs. not spam emails
- Cancer diagnosis (malignant vs. benign)
- Handwritten digit recognition (0-9)
Key concepts:
- Classes: distinct categories or labels
- Features: characteristics or attributes of the data
- Decision boundary: boundary that separates classes in feature space

Classification Algorithms

Logistic Regression:
- Uses logistic function to model probability of class membership
- Linear decision boundary
Decision Trees:
- Hierarchical representation of decisions
- Classify instances by traversing the tree
Random Forest:
- Ensemble of decision trees
- Improved accuracy and robustness
Support Vector Machines (SVMs):
- Find hyperplane that maximally separates classes
- Can be kernelized for non-linear boundaries