Supervised Learning and Classification

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Questions and Answers

الهدف من التعلم تحت الإشراف هو?

  • توليد نماذج من البيانات
  • توقع القيم العشوائية للإدخال الجديد
  • تعلم الخريطة بين بيانات الإدخال وال_etiquettes_ الخروج (correct)
  • lernen من البيانات غير المسلية

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

  • التنبؤ بالوقت الحقيقي
  • تحليل الشبكة
  • تدقيق النصوص
  • التصنيف (correct)

ما هو الاختلاف الرئيسي بين الشجرة القرارية وغابة عشوائية؟

  • عمر الشجرة
  • مستوى التعقيد (correct)
  • ال 정확ية
  • عدد الشجرة

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

<p>اللوغستية الانحدار (A)</p> Signup and view all the answers

ما هو الغرض من دالة الحد الأقصى في خوارزمية SVM؟

<p>تخصيص الحدود الأقصى (C)</p> Signup and view all the answers

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

<p>التنبؤ بالقيمة (B)</p> Signup and view all the answers

What is the primary goal of supervised learning?

<p>To learn a mapping between input data and the corresponding output labels (D)</p> Signup and view all the answers

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

<p>Multi-Label Classification (B)</p> Signup and view all the answers

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

<p>Overfitting (A)</p> Signup and view all the answers

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

<p>The type of output variable predicted (B)</p> Signup and view all the answers

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

<p>To evaluate the model's performance (A)</p> Signup and view all the answers

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

<p>Regression (C)</p> Signup and view all the answers

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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

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