What is Artificial Intelligence (AI) and Machine Learning (ML)?
AI is the broader field of creating intelligent agents capable of mimicking human-like cognitive functions.
ML is a subset of AI that focuses on developing algorithms and models that enable computers to learn from data and make predictions or decisions.
Explain the difference between supervised, unsupervised, and reinforcement learning.
Supervised Learning: Involves training a model on labeled data, where the model learns to make predictions based on input-output pairs.
Unsupervised Learning: Involves discovering patterns or relationships in unlabeled data, often used for clustering and dimensionality reduction.
Reinforcement Learning: Involves training agents to make a sequence of decisions to maximize a reward signal in an environment.
What is overfitting in machine learning, and how can it be prevented?
Overfitting occurs when a model learns the training data too well but fails to generalize to unseen data. To prevent it, techniques such as cross-validation, regularization, and having more diverse data can be used.
What is bias-variance trade-off in machine learning?
The bias-variance trade-off is a fundamental concept in ML. It refers to the balance between underfitting (high bias, low variance) and overfitting (low bias, high variance). Finding the right trade-off is crucial for model performance.
What is a decision tree, and how does it work?
A decision tree is a supervised learning algorithm used for classification and regression tasks. It works by recursively splitting the data into subsets based on the most significant feature to make decisions.
Explain the concept of feature engineering.
Feature engineering is the process of selecting, transforming, or creating new features from the raw data to improve the performance of machine learning models. It involves domain knowledge and creativity.
What is the curse of dimensionality, and how does it affect machine learning algorithms?
The curse of dimensionality refers to the challenges and problems that arise when dealing with high-dimensional data. It can lead to increased computational complexity, overfitting, and difficulties in visualization and interpretation.
What is cross-validation, and why is it important in machine learning?
Cross-validation is a technique for assessing a model’s performance by splitting the data into multiple subsets and repeatedly training and testing the model on different partitions. It helps evaluate a model’s generalization ability.
What is deep learning, and how does it differ from traditional machine learning?
Deep learning is a subfield of machine learning that focuses on neural networks with multiple layers (deep neural networks). It excels at tasks involving unstructured data, such as images, audio, and text, and often requires large amounts of labeled data.
Explain the concept of gradient descent in the context of optimization in machine learning.
Gradient descent is an optimization algorithm used to find the minimum of a cost function by iteratively adjusting model parameters in the direction of the steepest decrease in the cost function’s gradient.
What is a neural network activation function, and why is it important?
An activation function introduces non-linearity to a neural network by determining the output of a neuron. It is essential because it allows neural networks to learn complex, non-linear relationships in data.
What is the difference between precision and recall in binary classification?
Precision is the ratio of true positive predictions to the total positive predictions made by a model. It measures the accuracy of positive predictions.
Recall is the ratio of true positive predictions to the total actual positive instances. It measures a model’s ability to find all positive instances.
What are hyperparameters in machine learning, and how are they different from model parameters?
Hyperparameters are settings or configurations that are set before training a model. They control aspects like model complexity and training behavior. Model parameters, on the other hand, are learned from data during training.
What is transfer learning in deep learning?
Transfer learning is a technique where a pre-trained neural network, trained on a large dataset for a specific task, is adapted or fine-tuned for a different but related task. It leverages the knowledge gained from the original task to improve performance on the new task.
How do you evaluate the performance of a classification model?
Classification model performance can be evaluated using metrics such as accuracy, precision, recall, F1-score, and the ROC curve. The choice of metrics depends on the problem and the importance of false positives and false negatives.
What Are the Different Types of Machine Learning?
There are three types of machine learning:
Supervised Learning
In supervised machine learning, a model makes predictions or decisions based on past or labeled data. Labeled data refers to sets of data that are given tags or labels, and thus made more meaningful.
Unsupervised Learning
In unsupervised learning, we don’t have labeled data. A model can identify patterns, anomalies, and relationships in the input data.
Reinforcement Learning
Using reinforcement learning, the model can learn based on the rewards it received for its previous action.
Consider an environment where an agent is working. The agent is given a target to achieve. Every time the agent takes some action toward the target, it is given positive feedback. And, if the action taken is going away from the goal, the agent is given negative feedback.
What is Overfitting, and How Can You Avoid It?
The Overfitting is a situation that occurs when a model learns the training set too well, taking up random fluctuations in the training data as concepts. These impact the model’s ability to generalize and don’t apply to new data.
When a model is given the training data, it shows 100 percent accuracy—technically a slight loss. But, when we use the test data, there may be an error and low efficiency. This condition is known as overfitting.
There are multiple ways of avoiding overfitting, such as:
Regularization. It involves a cost term for the features involved with the objective function
Making a simple model. With lesser variables and parameters, the variance can be reduced
Cross-validation methods like k-folds can also be used
If some model parameters are likely to cause overfitting, techniques for regularization like LASSO can be used that penalize these parameters
What is ‘training Set’ and ‘test Set’ in a Machine Learning Model? How Much Data Will You Allocate for Your Training, Validation, and Test Sets?
There is a three-step process followed to create a model:
Train the model
Test the model
Deploy the model
The training set is examples given to the model to analyze and learn
The test set is used to test the accuracy of the hypothesis generated by the model
70% of the total data is typically taken as the training dataset
Remaining 30% is taken as testing dataset
Consider a case where you have labeled data for 1,000 records. One way to train the model is to expose all 1,000 records during the training process. Then you take a small set of the same data to test the model, which would give good results in this case.
But, this is not an accurate way of testing. So, we set aside a portion of that data called the ‘test set’ before starting the training process. The remaining data is called the ‘training set’ that we use for training the model. The training set passes through the model multiple times until the accuracy is high, and errors are minimized.
Now, we pass the test data to check if the model can accurately predict the values and determine if training is effective. If you get errors, you either need to change your model or retrain it with more data.
Regarding the question of how to split the data into a training set and test set, there is no fixed rule, and the ratio can vary based on individual preferences.
How Do You Handle Missing or Corrupted Data in a Dataset?
One of the easiest ways to handle missing or corrupted data is to drop those rows or columns or replace them entirely with some other value.
There are two useful methods in Pandas:
IsNull() and dropna() will help to find the columns/rows with missing data and drop them
Fillna() will replace the wrong values with a placeholder value
How Can You Choose a Classifier Based on a Training Set Data Size?
When the training set is small, a model that has a right bias and low variance seems to work better because they are less likely to overfit.
For example, Naive Bayes works best when the training set is large. Models with low bias and high variance tend to perform better as they work fine with complex relationships.
Explain the Confusion Matrix with Respect to Machine Learning Algorithms.
A confusion matrix (or error matrix) is a specific table that is used to measure the performance of an algorithm. It is mostly used in supervised learning; in unsupervised learning, it’s called the matching matrix.
The confusion matrix has two parameters:
Actual
Predicted
It also has identical sets of features in both of these dimensions.
What Is a False Positive and False Negative and How Are They Significant?
False positives are those cases that wrongly get classified as True but are False.
False negatives are those cases that wrongly get classified as False but are True.
In the term ‘False Positive,’ the word ‘Positive’ refers to the ‘Yes’ row of the predicted value in the confusion matrix. The complete term indicates that the system has predicted it as a positive, but the actual value is negative.
What Are the Three Stages of Building a Model in Machine Learning?
The three stages of building a machine learning model are:
Model Building
Choose a suitable algorithm for the model and train it according to the requirement
Model Testing
Check the accuracy of the model through the test data
Applying the Model
Make the required changes after testing and use the final model for real-time projects
Here, it’s important to remember that once in a while, the model needs to be checked to make sure it’s working correctly. It should be modified to make sure that it is up-to-date.
What is Deep Learning?
The Deep learning is a subset of machine learning that involves systems that think and learn like humans using artificial neural networks. The term ‘deep’ comes from the fact that you can have several layers of neural networks.
One of the primary differences between machine learning and deep learning is that feature engineering is done manually in machine learning. In the case of deep learning, the model consisting of neural networks will automatically determine which features to use (and which not to use).
How Will You Know Which Machine Learning Algorithm to Choose for Your Classification Problem?
While there is no fixed rule to choose an algorithm for a classification problem, you can follow these guidelines:
If accuracy is a concern, test different algorithms and cross-validate them
If the training dataset is small, use models that have low variance and high bias
If the training dataset is large, use models that have high variance and little bias
What Are the Applications of Supervised Machine Learning in Modern Businesses?
Applications of supervised machine learning include:
Email Spam Detection
Here we train the model using historical data that consists of emails categorized as spam or not spam. This labeled information is fed as input to the model.
Healthcare Diagnosis
By providing images regarding a disease, a model can be trained to detect if a person is suffering from the disease or not.
Sentiment Analysis
This refers to the process of using algorithms to mine documents and determine whether they’re positive, neutral, or negative in sentiment.
Fraud Detection
By training the model to identify suspicious patterns, we can detect instances of possible fraud.
What is Semi-supervised Machine Learning?
Supervised learning uses data that is completely labeled, whereas unsupervised learning uses no training data.
In the case of semi-supervised learning, the training data contains a small amount of labeled data and a large amount of unlabeled data.
What Are Unsupervised Machine Learning Techniques?
There are two techniques used in unsupervised learning: clustering and association.
Clustering
Clustering problems involve data to be divided into subsets. These subsets, also called clusters, contain data that are similar to each other. Different clusters reveal different details about the objects, unlike classification or regression.
Association
In an association problem, we identify patterns of associations between different variables or items.
For example, an e-commerce website can suggest other items for you to buy, based on the prior purchases that you have made, spending habits, items in your wishlist, other customers’ purchase habits, and so on.
What is the Difference Between Supervised and Unsupervised Machine Learning?
Supervised learning – This model learns from the labeled data and makes a future prediction as output
Unsupervised learning – This model uses unlabeled input data and allows the algorithm to act on that information without guidance.
What Is ‘naive’ in the Naive Bayes Classifier?
The classifier is called ‘naive’ because it makes assumptions that may or may not turn out to be correct.
The algorithm assumes that the presence of one feature of a class is not related to the presence of any other feature (absolute independence of features), given the class variable.
For instance, a fruit may be considered to be a cherry if it is red in color and round in shape, regardless of other features. This assumption may or may not be right (as an apple also matches the description).
Explain How a System Can Play a Game of Chess Using Reinforcement Learning.
Reinforcement learning has an environment and an agent. The agent performs some actions to achieve a specific goal. Every time the agent performs a task that is taking it towards the goal, it is rewarded. And, every time it takes a step that goes against that goal or in the reverse direction, it is penalized.
Earlier, chess programs had to determine the best moves after much research on numerous factors. Building a machine designed to play such games would require many rules to be specified.
With reinforced learning, we don’t have to deal with this problem as the learning agent learns by playing the game. It will make a move (decision), check if it’s the right move (feedback), and keep the outcomes in memory for the next step it takes (learning). There is a reward for every correct decision the system takes and punishment for the wrong one.
How Will You Know Which Machine Learning Algorithm to Choose for Your Classification Problem?
While there is no fixed rule to choose an algorithm for a classification problem, you can follow these guidelines:
If accuracy is a concern, test different algorithms and cross-validate them
If the training dataset is small, use models that have low variance and high bias
If the training dataset is large, use models that have high variance and little bias
How is Amazon Able to Recommend Other Things to Buy? How Does the Recommendation Engine Work?
Once a user buys something from Amazon, Amazon stores that purchase data for future reference and finds products that are most likely also to be bought, it is possible because of the Association algorithm, which can identify patterns in a given dataset.
When Will You Use Classification over Regression?
Classification is used when your target is categorical, while regression is used when your target variable is continuous. Both classification and regression belong to the category of supervised machine learning algorithms.
Examples of classification problems include:
Predicting yes or no
Estimating gender
Breed of an animal
Type of color
Examples of regression problems include:
Estimating sales and price of a product
Predicting the score of a team
Predicting the amount of rainfall
How Do You Design an Email Spam Filter?
Building a spam filter involves the following process:
The email spam filter will be fed with thousands of emails
Each of these emails already has a label: ‘spam’ or ‘not spam.’
The supervised machine learning algorithm will then determine which type of emails are being marked as spam based on spam words like the lottery, free offer, no money, full refund, etc.
The next time an email is about to hit your inbox, the spam filter will use statistical analysis and algorithms like Decision Trees and SVM to determine how likely the email is spam
If the likelihood is high, it will label it as spam, and the email won’t hit your inbox
Based on the accuracy of each model, we will use the algorithm with the highest accuracy after testing all the models.
What is a Random Forest?
A ‘random forest’ is a supervised machine learning algorithm that is generally used for classification problems. It operates by constructing multiple decision trees during the training phase. The random forest chooses the decision of the majority of the trees as the final decision.
Considering a Long List of Machine Learning Algorithms, given a Data Set, How Do You Decide Which One to Use?
There is no master algorithm for all situations. Choosing an algorithm depends on the following questions:
How much data do you have, and is it continuous or categorical?
Is the problem related to classification, association, clustering, or regression?
Predefined variables (labeled), unlabeled, or mix?
What is the goal?
What is Bias and Variance in a Machine Learning Model?
Bias
Bias in a machine learning model occurs when the predicted values are further from the actual values. Low bias indicates a model where the prediction values are very close to the actual ones.
Underfitting: High bias can cause an algorithm to miss the relevant relations between features and target outputs.
Variance
Variance refers to the amount the target model will change when trained with different training data. For a good model, the variance should be minimized.
Overfitting: High variance can cause an algorithm to model the random noise in the training data rather than the intended outputs.
What is the Trade-off Between Bias and Variance?
The bias-variance decomposition essentially decomposes the learning error from any algorithm by adding the bias, variance, and a bit of irreducible error due to noise in the underlying dataset.
Necessarily, if you make the model more complex and add more variables, you’ll lose bias but gain variance. To get the optimally-reduced amount of error, you’ll have to trade off bias and variance. Neither high bias nor high variance is desired.
High bias and low variance algorithms train models that are consistent, but inaccurate on average.
High variance and low bias algorithms train models that are accurate but inconsistent.
Define Precision and Recall.
Precision
Precision is the ratio of several events you can correctly recall to the total number of events you recall (mix of correct and wrong recalls).
Precision = (True Positive) / (True Positive + False Positive)
Recall
A recall is the ratio of the number of events you can recall the number of total events.
Recall = (True Positive) / (True Positive + False Negative)
What is a Decision Tree Classification?
A decision tree builds classification (or regression) models as a tree structure, with datasets broken up into ever-smaller subsets while developing the decision tree, literally in a tree-like way with branches and nodes. Decision trees can handle both categorical and numerical data.
What is Pruning in Decision Trees, and How Is It Done?
Pruning is a technique in machine learning that reduces the size of decision trees. It reduces the complexity of the final classifier, and hence improves predictive accuracy by the reduction of overfitting.
Pruning can occur in:
Top-down fashion. It will traverse nodes and trim subtrees starting at the root
Bottom-up fashion. It will begin at the leaf nodes
There is a popular pruning algorithm called reduced error pruning, in which:
Starting at the leaves, each node is replaced with its most popular class
If the prediction accuracy is not affected, the change is kept
There is an advantage of simplicity and speed
Briefly Explain Logistic Regression.
Logistic regression is a classification algorithm used to predict a binary outcome for a given set of independent variables.
The output of logistic regression is either a 0 or 1 with a threshold value of generally 0.5. Any value above 0.5 is considered as 1, and any point below 0.5 is considered as 0.
Explain the K Nearest Neighbour Algorithm.
K nearest neighbour algorithm is a classification algorithm that works in a way that a new data point is assigned to a neighbouring group to which it is most similar. In K nearest neighbours, K can be an integer greater than 1. So, for every new data point, we want to classify, we compute to which neighbouring group it is closest.
What is a Recommendation System?
Anyone who has used Spotify or shopped at Amazon will recognize a recommendation system: It’s an information filtering system that predicts what a user might want to hear or see based on choice patterns provided by the user.
What are Fuzzy Logic systems in AI?
Fuzzy logic (FL) is a method of reasoning that represents human reasoning. The approach of FL imitates the process of decision-making done by humans, including all possibilities between YES or NO’s digital values. It works on the levels of possibilities of input to achieve a definited output.
List out the various applications of AI?
The applications of AI are as follows:
- Chatbots
- Self-driving cars
- Image tagging
- AI in healthcare
- AI in eCommerce
- Human Resource Management
- Intelligent Cybersecurity
- AI to enhance workplace communication
- Facial expression recognition
- Natural Language Processing, and many more.
What are the programming languages used for Artificial Intelligence?
The following are the best AI programming languages used for Artificial Intelligence:
- Python
- Java
- R
- Prolog
- Lisp
- AIML
- STRIPS
How many Types of Artificial Intelligence are there? What are they?
There are four types of Artificial Intelligence as follows:
- Reactive Machines AI
- Limited Memory AI
- Theory of Mind AI
- Self Aware AI.
What are the stages of learning AI?
The following are the stages of learning AI:
Artificial General Intelligence (AGI): It is also known as Strong AI, which is considered a threat to many scientists’ human existence. It is an evolution of AI where machines can think and make decisions just like humans.
Artificial Normal Intelligence (ANI): It is also known as Weak AI that can perform only a defined activity set. It does not perform any thinking ability; instead, it performs a set of pre-defined functions.
Artificial Super Intelligence (ASI): ASI can perform everything that a human can do. Alpha 2 is an example of ASI, which is the first humanoid ASI robot.
What is an intelligent agent in Artificial Intelligence?
An Intelligent Agent (IA) refers to an autonomous entity that acts as a directing its activity to achieve goals as an agent upon an environment using observation by actuators and sensors.
What is the difference between Strong AI and Weak AI?
With strong AI, machines can think and perform tasks as their own, as humans do. With Weak AI, the machines cannot perform tasks independently; instead, it depends heavily on human interference.
Strong AI has a complex algorithm that helps it act in various situations, whereas Weak AIs are pre-programmed by humans.
Define an expert system in AI?
In Artificial Intelligence, an expert system is a computer system that imitates human experts’ decision-making ability. Expert systems are developed to solve problems by reasoning the bodies of knowledge, represented mainly as an if-then formula instead of conventional procedural code.
What are the characteristics of expert systems?
An expert system is designed to have the following characteristics:
- High-level performance
- Good Reliability
- Adequate Response time
- Linked with Metaknowledge
- Domain Specificity
- Understandable
- Justified Reasoning
- Expertise knowledge
- Special Programming Languages
- Use of symbolic representations.
What is A* Search Algorithms in AI?
A* is formulated with weighted graphs, which means it can find the best path involving the smallest cost in terms of time and distance. This makes A* algorithm in artificial intelligence an informed search algorithm for best-first search. A* search algorithm separates it from other traversal techniques as it has a brain.
What are the various domains of Artificial Intelligence?
The following are the various domains of Artificial Intelligence:
- Machine Learning
- Robotics
- Fuzzy logic systems
- Neural Networks
- Expert Systems
- Natural Language Processing.
List out various search algorithms in AI?
The various search algorithms in AI are:
- Breadth-First Search
- Bidirectional Search
- Depth-First Search
- Uniform Cost Search
- Heuristic Evaluation Functions
- Pure Heuristic Search
- Iterative Deepening Depth-First Search
- Comparison of various Algorithms Complexities
- Local Search Algorithms.
What are the various components of the Expert system?
An expert system includes three components of Expert System:
User Interface: The User Interface allows the user to communicate with the expert system to find the solutions for a complex problem
Knowledge Base: The Knowledge Base is a kind of Storage that is used to store high-quality and domain-specific knowledge
Inference Engine: It is the main processing unit of an expert system. It enables various inference rules to the knowledge base to bring a conclusion. The system extracts the information from the KB with the inference engine
List out the two various kinds of steps that we make in constructing a plan?
The following are two various steps that we make in constructing a plan:
- Add an operator
- Add ordering constraints between operators.
What are the advantages of an expert system?
The following are the advantages of an expert system:
- Memory
- Fast response
- Consistency
- Logic
- Ability to reason
- Unbiased in nature
Explain Artificial Intelligence and give its applications.
Artificial Intelligence (AI) is a field of Computer Science focuses on creating systems that can perform tasks that would typically require human intelligence, such as recognizing speech, understanding natural language, making decisions, and learning. We use AI to build various applications, including image and speech recognition, natural language processing (NLP), robotics, and machine learning models like neural networks.
How are machine learning and AI related?
Machine learning and Artificial Intelligence (AI) are closely related but distinct fields within the broader domain of computer science. AI includes not only machine learning but also other approaches, like rule-based systems, expert systems, and knowledge-based systems, which do not necessarily involve learning from data. Many state-of-the-art AI systems are built upon machine learning techniques, as these approaches have proven to be highly effective in tackling complex, data-driven problems.
What is Deep Learning based on?
Deep learning is a subfield of machine learning that focuses on the development of artificial neural networks with multiple layers, also known as deep neural networks. These networks are particularly effective in modeling complex, hierarchical patterns and representations in data. Deep learning is inspired by the structure and function of the human brain, specifically the biological neural networks that make up the brain.
How many layers are in a Neural Network?
Neural networks are one of many types of ML algorithms that are used to model complex patterns in data. They are composed of three layers — input layer, hidden layer, and output layer.
Explain TensorFlow.
TensorFlow is an open-source platform developed by Google designed primarily for high-performance numerical computation. It offers a collection of workflows that can be used to develop and train models to make machine learning robust and efficient. TensorFlow is customizable, and thus, helps developers create experiential learning architectures and work on the same to produce desired results.
What are the pros of cognitive computing?
Cognitive computing is a type of AI that mimics human thought processes. We use this form of computing to solve problems that are complex for traditional computer systems. Some major benefits of cognitive computing are:
It is the combination of technology that helps to understand human interaction and provide answers.
Cognitive computing systems acquire knowledge from the data.
These computing systems also enhance operational efficiency for enterprises.
What’s the difference between NLP and NLU?
Natural Language Processing (NLP) and Natural Language Understanding (NLU) are two closely related subfields within the broader domain of Artificial Intelligence (AI), focused on the interaction between computers and human languages. Although they are often used interchangeably, they emphasize different aspects of language processing.
NLP deals with the development of algorithms and techniques that enable computers to process, analyze, and generate human language. NLP covers a wide range of tasks, including text analysis, sentiment analysis, machine translation, summarization, part-of-speech tagging, named-entity recognition, and more. The goal of NLP is to enable computers to effectively handle text and speech data, extract useful information, and generate human-like language outputs.
While, NLU is a subset of NLP that focuses specifically on the comprehension and interpretation of meaning from human language inputs. NLU aims to disambiguate the nuances, context, and intent in human language, helping machines grasp not just the structure but also the underlying meaning, sentiment, and purpose. NLU tasks may include sentiment analysis, question-answering, intent recognition, and semantic parsing.
Give some examples of weak and strong AI.
Some examples of weak AI include rule-based systems and decision trees. Basically, those systems that require an input come under weak AI. On the other hand, a strong AI includes neural networks and deep learning, as these systems and functions can teach themselves to solve problems.
What is the need of data mining?
Data mining is the process of discovering patterns, trends, and useful information from large datasets using various algorithms, statistical methods, and machine learning techniques. It has gained significant importance due to the growth of data generation and storage capabilities. The need for data mining arises from several aspects, including decision-making.
Name some sectors where data mining is applicable.
There are many sectors where data mining is applicable, including:
Healthcare -It is used to predict patient outcomes, detection of fraud and abuse, measure the effectiveness of certain treatments, and develop patient and doctor relationships.
Finance -The finance and banking industry depends on high-quality, reliable data. It can be used to predict stock prices, predict loan payments and determine credit ratings.
Retail- It is used to predict consumer behavior, noticing buying patterns to improve customer service and satisfaction.
What are the components of NLP?
There are three main components to NLP:
Language understanding – This defines the ability to interpret the meaning of a piece of text
Language generation – This is helpful in producing text that is grammatically correct and conveys the intended meaning.
Language processing – This helps in performing operations on a piece of text, such as tokenization, lemmatization, and part-of-speech tagging.
What is the full form of LSTM?
LSTM stands for Long Short-Term Memory, and it is a type of recurrent neural network (RNN) architecture that is widely used in artificial intelligence and natural language processing. LSTM networks have been successfully used in a wide range of applications, including speech recognition, language translation, and video analysis, among others.
What is Artificial Narrow Intelligence (ANI)?
Artificial Narrow Intelligence (ANI), also known as Weak AI, refers to AI systems that are designed and trained to perform a specific task or a narrow range of tasks. These systems are highly specialized and can perform their designated task with a high degree of accuracy and efficiency. This type of technology is also known as Weak AI.
What is a data cube?
A data cube is a multidimensional (3D) representation of data that can be used to support various types of analysis and modeling. Data cubes are often used in machine learning and data mining applications to help identify patterns, trends, and correlations in complex datasets.
What is the difference between model accuracy and model performance?
Model accuracy refers to how often a model correctly predicts the outcome of a specific task on a given dataset. Model performance, on the other hand, is a broader term that encompasses various aspects of a model’s performance, including its accuracy, precision, recall, F1 score, AUC-ROC, etc. Depending on the problem you’re solving, one metric may be more important than the other.