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Questions and Answers

What is the formula for Binary Cross-Entropy Loss?

$-\sum y \ln(1 - p) + (1 - y)\ln(p)$

$-\sum (y - \hat{y})^2$

$-\sum y \ln(p) + (1 - y)\ln(1 - p)$ (correct)

$\sum (y - p)^2$

Which function is used to convert logits into probabilities for multiclass classification?

Tanh Function

ReLU Function

Sigmoid Function

Softmax Function (correct)

Which of the following accurately represents the accuracy metric in classification models?

$\frac{TP + FN}{TP + TN + FP}$

$\frac{TP + TN}{TP + FP + TN + FN}$ (correct)

$\frac{TP}{TP + FP}$

$\frac{TP + FP}{TP + TN}$

What does the F1-Score combine to evaluate the model's performance?

Precision and Recall Signup and view all the answers

In the context of neural networks, what does the derivative of the sigmoid function represent?

Changing response to input Signup and view all the answers

Which activation function has a range of (0,1)?

Sigmoid Function Signup and view all the answers

What is the main purpose of backpropagation in neural networks?

To optimize the model weights Signup and view all the answers

What term describes the process of calculating probabilities of class membership using features in Naive Bayes?

Bayesian Inference Signup and view all the answers

Which of the following correctly expresses the formula for Precision?

$\frac{TP}{TP + FP}$ Signup and view all the answers

The Leaky ReLU activation function is characterized by which of the following features?

Allows a small gradient when inputs are less than zero Signup and view all the answers

What is the primary method for predicting values when k is greater than 1 in a KNN regression model?

Get the average value of k nearest neighbors Signup and view all the answers

Which distance function is specifically used to measure similarity between two vectors based on their direction?

Cosine Distance Signup and view all the answers

Which of the following is a disadvantage of using KNN?

It considers all features equally without assessment of relevance. Signup and view all the answers

In linear regression, what does the parameter θ0 represent?

The intercept of the regression line Signup and view all the answers

What is the goal of training a linear regression model?

To fit a line that captures the relationship in the data Signup and view all the answers

Which potential issue arises when using salary as a feature in the prediction model without normalization?

Salary can unduly influence the calculation of distances. Signup and view all the answers

What type of data can KNN algorithms handle for predictions?

Both continuous labels and categorical labels Signup and view all the answers

What is the significance of 'k' in the KNN algorithm?

It indicates the number of nearest neighbors to consider for prediction. Signup and view all the answers

In which scenario would you use bucketing in KNN predictions?

When labels are continuous and cannot be classified. Signup and view all the answers

What does forward propagation primarily involve?

Computing the predictions based on input data. Signup and view all the answers

In a neural network, what does the bias term represent?

A constant input to the neuron. Signup and view all the answers

How is the score of a neuron (Z) calculated during forward propagation?

By adding the bias term to the weighted sum of inputs. Signup and view all the answers

What activation function is used in the example provided for calculating the output of each neuron?

Sigmoid Signup and view all the answers

How is the final prediction (y) determined in the provided example?

By comparing the output of the activation function to a threshold. Signup and view all the answers

What is represented by the variable θ in the context of forward propagation?

The weights of the features/inputs in the network. Signup and view all the answers

What can be said about the vectorization of values in forward propagation?

It allows for simultaneous calculations of multiple instances. Signup and view all the answers

If the score Z1 for the first neuron is calculated as 0.07, what would be the output after applying the sigmoid function?

0.51749 Signup and view all the answers

What is the purpose of the sigmoid function in logistic regression?

To map pre-sigmoid values to probabilities Signup and view all the answers

What happens if the output of the sigmoid function is greater than or equal to 0.5?

The instance is classified as positive Signup and view all the answers

Why is the natural logarithm used in the binary cross entropy loss function?

To preserve order for small probability values Signup and view all the answers

In multinomial logistic regression, how are the classes handled?

Each class is modeled with a separate classifier but trained together Signup and view all the answers

What is the output of the Softmax function designed to achieve?

Convert scores into probabilities that sum to 1 Signup and view all the answers

What does a True Positive represent in a confusion matrix?

Positive instances predicted correctly Signup and view all the answers

When evaluating a classification model, what is precision calculated from?

True Positives and False Positives Signup and view all the answers

How does gradient descent in logistic regression generally relate to linear regression?

Gradient descent operates similarly in both cases Signup and view all the answers

What do false negatives indicate in the context of a confusion matrix?

Positive instances predicted as negative Signup and view all the answers

What is the main goal of the binary cross entropy loss function?

Match predicted probabilities to actual outcomes Signup and view all the answers

What does the decision boundary represent in a logistic regression model?

The limit of feature values where classification switches Signup and view all the answers

Why is the output of the sigmoid function important in classification tasks?

It indicates the model's confidence level Signup and view all the answers

What is a characteristic of the decision boundaries created by logistic regression?

Always straight lines in the feature space Signup and view all the answers

What is a primary reason for splitting training data into train, validation, and test sets?

To prevent overfitting by evaluating performance on unseen data. Signup and view all the answers

Which approach is better when using web data for training a model?

Only include web data in the training set while keeping user data separate. Signup and view all the answers

What is the purpose of conducting manual error analysis after deploying a model?

To compare real-world data against expected outcomes. Signup and view all the answers

Which is NOT a suggested method for hyperparameter tuning?

Adjusting hyperparameters based entirely on training set performance. Signup and view all the answers

What is crucial to consider when augmenting training data using external sources?

Guaranteeing the external data reflects the actual scenarios the model will face. Signup and view all the answers

What technique can reduce error in an animal classification model?

Manual examination of mislabeled data to identify common errors. Signup and view all the answers

What approach ensures that data used for training and testing share similarities?

Shuffling all data before splitting. Signup and view all the answers

In the context of training a model, what is the main advantage of error analysis?

It allows for an understanding of model limitations and necessary improvements. Signup and view all the answers

Which statement about training with imbalanced data is true?

Using only majority class examples can lead to biased outcomes. Signup and view all the answers

How can one effectively fine-tune hyperparameters to improve model performance?

By evaluating performance on the validation set after testing various combinations. Signup and view all the answers

What is the primary goal of backward propagation in a neural network?

To adjust weights to reduce the loss function Signup and view all the answers

Which mathematical principle is primarily used to compute the effect of each parameter on the loss in backward propagation?

Chain Rule Signup and view all the answers

In the expression $f(x, y, z) = (x + y) z$, what does $q$ represent?

The sum of $x$ and $y$ Signup and view all the answers

How is the derivative of the prediction $ar{y}$ with respect to $a_1$ defined in backward propagation?

1 Signup and view all the answers

What does the derivative $rac{ ext{d}a}{ ext{d}W_{11}}$ represent?

How the activation $a$ changes with respect to weight $W_{11}$ Signup and view all the answers

What is the role of bias $b$ in the neuron output $z$?

To introduce flexibility in the score calculation Signup and view all the answers

Which of the following equations shows how $W_{11}$ affects $z_1$?

$rac{ ext{d}z_1}{ ext{d}W_{11}} = a_1$ Signup and view all the answers

What does the derivative $rac{ ext{d}a_1}{ ext{d}z_1}$ represent in the context of a sigmoid function?

$ ext{sigmoid}(z_1)(1 - ext{sigmoid}(z_1))$ Signup and view all the answers

When computing derivatives in a network, which operations should be computed first?

Derivatives of neighboring operations Signup and view all the answers

What notation is commonly used to represent weights in a neural network?

W Signup and view all the answers

During backward propagation, what is updated along with the weights?

Bias terms Signup and view all the answers

How can the entire process of calculating derivatives in backpropagation be summarized for every layer?

All computations can be vectorized. Signup and view all the answers

What is the significance of computing $rac{ ext{d}z_1}{ ext{d}b_1}$ in the context of neural networks?

To determine the bias adjustment impact on neuron scores Signup and view all the answers

When using the chain rule in backpropagation, what is the final output calculation represented as?

$rac{ ext{d}a[i]}{ ext{d}z[i]} = ext{sigmoid}(z[i])(1 - ext{sigmoid}(z[i]))$ Signup and view all the answers

Study Notes

K-Nearest Neighbors (KNN)

A simple supervised machine learning model
Predicts based on the similarity to its nearest neighbors
Stores all training data in memory
Identifies if a person has been previously dated based on similar features
Measures similarity through Euclidean or Manhattan distance
The model may be uncertain if multiple instances have the same distance but differing labels

Linear Regression

A supervised regression model
Builds a predictive model in the form of a line (1 feature) or plane (2 features)
Goal: to find the line/plane that fits most of the data
Useful in determining the direction/rotation and offset of the line
Can be expanded to multiple features, becoming a hyperplane

Logistic Regression

A supervised classification model
Predicts the probability of an instance belonging to a certain category
Uses sigmoid function to map the output linear equation to values from 0 to 1 (excluded)

Naive Bayes

Predicts the probability that an instance belongs to a certain class
Uses Bayes' rule for calculating posterior probability and assumptions regarding the independency of features
Can classify text data (e.g., spam detection)

Decision Trees

A supervised classification or regression model
Creates a tree-like structure with nodes representing questions about features, which lead to leaves representing classifications or predictions
Uses impurity measures like Gini Index or Shannon Entropy to determine which questions are most useful in classifying data.
More flexible (capable of handling both discrete and continuous values than a linear model), likely to overfit

Neural Networks

Model that learns weights and biases
Consists of layers of neurons, and connections between them Activation functions (such as sigmoid, tanh, ReLU) are used to introduce non-linearity

Bias-Variance Tradeoff

Bias: describes the expected error due to the model's inability to capture real-world patterns or relationships in the data
Variance: describes the error due to the sensitivity of the model to the training data
The model should be as simple as possible in order to achieve a good balance of these errors

Regularization

Used in machine learning to reduce overfitting by introducing additional cost to the model's complexity
Can be applied to regression and other problems
Common methods include Ridge and Lasso Regression

Evaluation of Classification Models

Confusion Matrix: a table that summarizes the performance of a classification model
Accuracy: ratio of correctly classified instances to the total number of instances
Precision: the fraction of predicted positive instances which are actually positive
Recall: the fraction of actual positive instances which are correctly predicted
F1-Score: a harmonic mean of precision and recall, useful in imbalanced data

Ensemble Learning

Stacking and Bagging: combine multiple models together, typically by using a weighted vote to improve the resulting prediction
Boosting: uses previous models' mistakes to build better models

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