What is regression in the context of machine learning? An unsupervised learning technique for clustering data A technique for reducing the dimensionality of data A method for classifying data into discrete categories A supervised learning technique used to predict a continuous outcome variable based on input variables

A supervised learning technique used to predict a continuous outcome variable based on input variables

What's the main goal when performing regression? To reduce the number of features in a dataset To cluster data points into groups To classify data into different categories To build a prediction model that can estimate the Y value

To build a prediction model that can estimate the Y value

In regression, what do X and Y represent? X and Y both represent dependent variables X represents independent variables, and Y represents the dependent variable X and Y both represent independent variables X represents the dependent variable, and Y represents independent variables

X represents independent variables, and Y represents the dependent variable

What library is used for linear regression in the course? Scikit-learn's LinearRegression model TensorFlow's LinearModel Keras's LinearLayer PyTorch's LinearRegression

Scikit-learn's LinearRegression model

What are the basic steps in building a regression model in Python? Define problem, gather data, preprocess data, train model, test model Collect data, clean data, visualize data, train model, evaluate model Generate synthetic data, pick a model, set hyperparameters, prepare data, fit the model, predict Identify features, select model, train model, validate model, deploy model

Generate synthetic data, pick a model, set hyperparameters, prepare data, fit the model, predict

How do you interpret the regression output (coefficients and intercept)? The coefficient is the average change in Y; The intercept is the median of Y The coefficient represents how much Y changes with a one-unit increase in X; The intercept is the Y value when all X values are 0 The coefficient is the product of X and Y; The intercept is the difference between X and Y The coefficient is the sum of all X values; The intercept is the sum of all Y values

The coefficient represents how much Y changes with a one-unit increase in X; The intercept is the Y value when all X values are 0

What is the predicted value in regression called? Y, representing the actual output β, representing the regression coefficient X, representing the input variable Ŷ (Y hat), representing the model's predicted output

Ŷ (Y hat), representing the model's predicted output

How do you evaluate the accuracy of your regression model? By using metrics like mean squared error (MSE) - the lower the MSE, the better the model's predictions By calculating the sum of squared errors (SSE) By using the confusion matrix By measuring the correlation coefficient

By using metrics like mean squared error (MSE) - the lower the MSE, the better the model's predictions

What's a good next step after simple regression? Try regression with a real-world dataset and multiple features to build a more complex model Implement clustering algorithms Switch to classification tasks Focus on data preprocessing techniques

Try regression with a real-world dataset and multiple features to build a more complex model

What is a decision tree in machine learning? A clustering algorithm that groups data points A neural network with multiple layers A linear model that predicts continuous outcomes A model that splits data into branches based on decision rules

A model that splits data into branches based on decision rules

Comprehensive Guide to Machine Learning Concepts and Techniques

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Computer Science - Software Engineering

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Cards in this deck(95)

What is regression in the context of machine learning?

What's the main goal when performing regression?

In regression, what do X and Y represent?

What library is used for linear regression in the course?

What are the basic steps in building a regression model in Python?

How do you interpret the regression output (coefficients and intercept)?

What is the predicted value in regression called?

How do you evaluate the accuracy of your regression model?

What's a good next step after simple regression?

What is a decision tree in machine learning?

How does a decision tree work?

What does each internal node represent in a decision tree?

What does each leaf node represent in a decision tree?

What are the key advantages of using decision trees?

What is a common disadvantage of decision trees?

What does overfitting mean in the context of decision trees?

What are some ways to prevent or fix overfitting in decision trees?

What is a root node in a decision tree?

What are leaf or terminal nodes in a decision tree?

What are entropy and Gini index used for in decision trees?

Can decision trees be used for both regression and classification problems?

What is pruning in decision trees?

What does cross-validation help with in decision trees?

What is underfitting, and how is it different from overfitting?

How does a decision tree choose the feature to split on?

What is precision in the context of model evaluation?

What is recall in the context of model evaluation?

What is the F1 score and when is it useful?

What is a confusion matrix and what does it show?

What is cross-validation and why is it important?

What is a Random Forest model?

Why use a Random Forest model?

What's a pipeline in scikit-learn and why use it?

How does the K-Means clustering algorithm work?

What is a use case of the K-Means clustering algorithm?

When do you use supervised vs unsupervised learning?

What is the basic idea behind the K-Means algorithm?

Why is the term 'right' in K-Means clustering considered relative?

What does the model try to do when it's trained on the X and Y data?

What tool is used to run TensorFlow code without installing it locally?

What is a 'Sequential Model' in TensorFlow?

What are the three basic types of layers in a neural network?

What is the function of a hidden layer in a neural network?

What is an activation function in a neural network?

What is a deep neural network?

What is the MNIST dataset?

How does Scikit-Learn expect the data shape?

What is Y hat in regression?

What is Y in regression?

What will result in a single predictor variable in a DataFrame?

What does Scikit-Learn expect for features (X)?

What is the purpose of splitting data into training and testing sets?

What do the mean squared error (MSE) and R2 score indicate?

What is overfitting in the context of machine learning?

How can overfitting be avoided in models?

What is the hold out method in cross-validation?

What are the two weaknesses of the hold out method?

How does cross-validation fix the problem of the hold out method?

How can you improve value in relation to decision and tree?

What is linear regression?

In the linear regression equation y=ax+b, what does each term represent?

What is multidimensional linear regression?

What is basis function regression?

What are polynomial basis functions?

What are Gaussian basis functions?

What are decision trees in machine learning?

Why are they called 'Decision Trees'?

How does a decision tree work?

What are some applications of decision trees?

What are the advantages of decision trees?

What are the disadvantages of decision trees?

What is entropy in the context of decision trees?

How is entropy used in decision trees?

What does high entropy mean?

What does low entropy mean?

What is information gain in decision trees?

What does information gain measure?

What is Gini impurity in decision trees?

How does Gini impurity work in decision trees?

What is overfitting in the context of machine learning?

What is underfitting in the context of machine learning?

What are strategies to avoid overfitting in decision trees?

What is cross-validation in machine learning?

What is K-Fold Cross-Validation?

What does accuracy measure in model evaluation?

What is precision in the context of model evaluation?

What is recall in the context of model evaluation?

What is the F1 Score in model evaluation?

What is a confusion matrix in model evaluation?

What is a classification report in model evaluation?

What is the Random Forest algorithm?

What is Bootstrap Aggregating (Bagging)?

How are classification tasks handled in a Random Forest?

How are regression tasks handled in a Random Forest?

What are the advantages of Random Forest?

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Comprehensive Guide to Machine Learning Concepts and Techniques

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