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Questions and Answers
In linear regression, what is the primary goal of the model?
In linear regression, what is the primary goal of the model?
- To group the data into clusters
- To predict the target variable (correct)
- To estimate the probability of an event
- To classify data into categories
What distinguishes a dependent variable from an independent variable in linear regression?
What distinguishes a dependent variable from an independent variable in linear regression?
- There is no difference between dependent and independent variables in linear regression
- The independent variable is the output, while the dependent variable is the input
- The dependent variable is the output, while the independent variable is the input (correct)
- The dependent variable is categorical, while the independent variable is numerical
What is the purpose of Bayes rule in statistical modeling?
What is the purpose of Bayes rule in statistical modeling?
- To classify data into categories
- To estimate the parameters of a statistical model
- To group data into clusters
- To calculate the probability of an event occurring given some evidence (correct)
What does the loss function aim to achieve in a machine learning algorithm?
What does the loss function aim to achieve in a machine learning algorithm?
Which parameter is typically estimated in logistic regression?
Which parameter is typically estimated in logistic regression?
What characterizes the degree of a polynomial in polynomial regression?
What characterizes the degree of a polynomial in polynomial regression?
Why do we minimize the sum of squared errors in linear regression?
Why do we minimize the sum of squared errors in linear regression?
What represents a common loss function for linear regression models?
What represents a common loss function for linear regression models?
Which loss function is commonly used for binary classification problems?
Which loss function is commonly used for binary classification problems?
What role does a loss function play in machine learning algorithms?
What role does a loss function play in machine learning algorithms?
Flashcards
Goal of Linear Regression
Goal of Linear Regression
To predict the target variable.
Dependent vs Independent Variable
Dependent vs Independent Variable
The dependent variable is the output, while the independent variable is the input.
Bayes Rule Purpose
Bayes Rule Purpose
To calculate the probability of an event occurring given some evidence.
Loss Function in ML
Loss Function in ML
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Parameter in Logistic Regression
Parameter in Logistic Regression
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Polynomial Degree
Polynomial Degree
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Minimizing Sum of Squared Errors
Minimizing Sum of Squared Errors
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Common Loss Function in Linear Regression
Common Loss Function in Linear Regression
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Loss Function for Binary Classification
Loss Function for Binary Classification
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Role of Loss Function in ML
Role of Loss Function in ML
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Study Notes
Linear Regression
- Simple linear regression uses one predictor variable, while multiple linear regression uses multiple predictor variables.
- The goal of a linear regression model is to predict the target variable.
Variables in Linear Regression
- The dependent variable is the output, while the independent variable is the input.
Goal of Linear Regression
- The goal of linear regression is to minimize the sum of the squared errors between predicted and actual values.
Logistic Regression
- In logistic regression, the parameter being estimated is the coefficients.
Polynomial Regression
- The degree of the polynomial in polynomial regression is the order of the polynomial function.
Bayes Rule
- Bayes rule is used to calculate the probability of an event occurring given some evidence.
- The formula for Bayes rule is P(B|A) = P(A|B) * P(B) / P(A).
Loss Functions
- The role of a loss function in a machine learning algorithm is to optimize the model parameters.
- Mean squared error is a common loss function for linear regression.
- Cross-entropy loss is a common loss function for binary classification problems.
- The goal of minimizing the loss function in machine learning is to find the best model parameters that fit the data.
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