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What does regression primarily predict?
Which of the following best describes a label in machine learning?
What type of data consists of distinct categories without any natural ordering?
Which machine learning model is not typically associated with classification tasks?
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Which of the following is true about features in machine learning?
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What distinguishes ordinal data from categorical data?
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What is an example of a feature in a spam detector?
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What is typically used to encode categorical data?
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Which type of feature represents the loan amount in credit approval applications?
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What is the primary purpose of applying linear regression?
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In the context of handwritten digit recognition, which type of feature categorizes pixels based on color?
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What does the term 'hard feature engineering process' refer to in machine learning?
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In the equation for linear regression, what does 'b' represent?
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Which label is an output option for credit applications?
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What characteristic makes ordinal features distinct in machine learning?
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What does 'y’' represent in the adjusted linear regression equation?
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What is the primary purpose of feature engineering in machine learning?
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During which phase of a model's life is it trained using labeled examples?
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What are labeled examples primarily used for in machine learning?
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What does a model define in the context of machine learning?
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What is the purpose of taking a random sample when estimating a population characteristic, such as average height?
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What best describes the concept of convergence in the context of gradient descent?
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Which characteristic is NOT a property of a gradient in the context of gradient descent?
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What role does the learning rate play in the gradient descent algorithm?
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In gradient descent, which of the following statements is true about the update step?
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What type of plots do regression problems yield when considering loss versus weight?
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Which statement about the steps in the gradient descent algorithm is correct?
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What does the phrase 'greedy algorithm' imply in the context of taking steps towards a minimum in gradient descent?
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What is typically indicated by a 'smiley face' graph in the context of convex functions?
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What is the purpose of the learning rate in gradient descent algorithms?
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Which gradient descent method processes training data in small batches or individually?
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In logistic regression, what type of outcome does it predict?
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Which statement describes Batch Gradient Descent?
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What condition represents Class 1 in logistic regression's decision boundary equation?
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What is a characteristic of Mini-Batch Gradient Descent?
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What is the loss function used in the gradient descent approach mentioned?
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What indicates a limitation of logistic regression's output?
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Study Notes
Machine Learning Basic Terminologies
- Labels: Output that's predicted in a classification or regression task. Often denoted with the variable 'y'.
- Features: Input variables that describe the data. Denoted as x1, x2,...xn.
- Examples: A specific data instance, denoted as 'x' and often has an associated label. Examples can be labeled or unlabeled.
- Models: Define a relationship between features and labels. Have two phases – training and testing/inference.
Types of Features
- Categorical: Information that can be put into categories. Example: Red, blue, brown, yellow.
- Ordinal: Information that has a natural order. Example: Poor, satisfactory, good, excellent.
- Numeric: Can be expressed as an integer or real value. Example: Height, weight, temperature.
Feature Engineering
- Feature Engineering: Extracting and transforming variables from raw data to use for training and predictions.
Model Building: Gradient Descent
- Gradient Descent: An optimization algorithm that updates model parameters iteratively to minimize loss.
- Steps: Pick a random starting point for the model parameters, calculate the gradient of the loss function, update parameters, repeat steps 2 and 3 until convergence.
- Convergence: When the model stops improving its predictions.
- Learning Rate: Parameter that controls the step size at each iteration of gradient descent.
Types of Gradient Descent
- Batch Gradient Descent: Uses the entire training dataset for each iteration. Can be slow for large datasets.
- Stochastic Gradient Descent (SGD): Processes data in small batches or individual data points. Noisy but fast.
- Mini-Batch Gradient Descent: Compromise between Batch Gradient Descent and SGD. Uses a batch of a fixed size.
Logistic Regression
- What it is: Used for binary classification tasks. Predicts the probability of a binary event occurring (e.g., yes/no, 0/1, true/false).
- Input: Features values.
- Output: Probability from 0 to 1.
- Linear Classifier: The decision boundary is represented by a linear equation.
- Example: Predicting whether a student will pass or fail an exam based on the number of hours they study.
Linear Regression
- What it is: Attempts to model the relationship between two variables by fitting a linear equation.
- Example: Predicting temperature based on the number of cricket chirps.
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Equation: y' = w1x1 + w0
- y': predicted label
- w1: weight of feature 1
- w0: bias (intercept)
- x1: input feature value
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Description
This quiz covers essential terminologies in machine learning, including key concepts like labels, features, and models. Understand different types of features such as categorical, ordinal, and numeric, as well as the importance of feature engineering in model building. Test your knowledge and deepen your understanding of these foundational concepts.
