ML Algorithms
SVM
Support vector machines (SVMs) are a supervised learning algorithm commonly used for classification and regression tasks. They are versatile and powerful, and they have been successfully applied to a wide range of problems, including:
Text classification: Spam filtering, sentiment analysis
Image classification: Handwritten digit recognition, object detection
Bioinformatics: Gene expression analysis, protein structure prediction
Finance: Stock market prediction, fraud detection
How do SVMs work?
The goal of an SVM is to find a hyperplane that clearly separates two classes of data points. A hyperplane is a flat, high-dimensional surface that can be used to divide a space into two or more regions. In the case of SVMs, the hyperplane separates the two classes of data points so that the margin, or the distance between the hyperplane and the nearest data points from each class, is maximized.
To find the optimal hyperplane, SVMs use a technique called support vector selection. Support vectors are the data points that are closest to the hyperplane and that define the margin. By focusing on these support vectors, SVMs are able to find a hyperplane that is robust to noise and outliers.
Types of SVMs
There are two main types of SVMs:
Linear SVMs: These SVMs are used to find a linear hyperplane that separates the two classes of data points.
Nonlinear SVMs: These SVMs are used to find nonlinear hyperplanes by using kernel functions. Kernel functions map the data into a higher-dimensional space, where a linear hyperplane can be used to separate the data.
Advantages of SVMs
SVMs have several advantages over other machine learning algorithms:
High accuracy: SVMs are known for their ability to achieve high accuracy on a wide range of problems.
Robustness to noise: SVMs are relatively robust to noise and outliers.
Generalization ability: SVMs tend to generalize well to unseen data.
Versatility: SVMs can be used for both classification and regression tasks.
Disadvantages of SVMs
SVMs also have a few disadvantages:
Computational complexity: SVMs can be computationally expensive to train, especially for large datasets.
Sensitivity to feature scaling: SVMs can be sensitive to the scaling of the features in the dataset.
Difficulty interpreting the results: SVMs can be difficult to interpret, as they do not provide a direct relationship between the input features and the output.
Overall, SVMs are a powerful and versatile machine learning algorithm that has been successfully applied to a wide range of problems. They are a valuable tool for data scientists and machine learning practitioners.
Random forest
Random forest is a supervised learning algorithm that consists of a multitude of decision trees. It is an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time and outputting the mode of the class (or mean prediction) of the individual trees. Random forests correct for decision trees' habit of overfitting to their training set. Random forests generally outperform decision trees, but their accuracy is lower than gradient boosted trees.
How Random Forest Works
The basic idea behind random forest is that an ensemble of decision trees will perform better than any individual decision tree. This is because each tree in the forest is trained on a different subset of the data, and so each tree has a different perspective on the data. When the trees are combined, they are able to make more accurate predictions than any one tree could alone.
There are two main techniques used to create random forests: bagging and feature randomness.
Bagging: Bagging, or bootstrap aggregating, is a technique for reducing variance by averaging the predictions of multiple models. In the context of random forests, bagging involves creating multiple decision trees, each of which is trained on a different random subset of the data. The final prediction of the random forest is the majority vote of the individual trees.
Feature randomness: Feature randomness is a technique for reducing overfitting by introducing randomness into the feature selection process. In the context of random forests, feature randomness involves considering a random subset of the available features at each split in the decision tree. This helps to prevent the trees from becoming too reliant on any one feature.
Advantages of Random Forest
Random forests are very accurate and can outperform other machine learning algorithms on a variety of tasks.
Random forests are relatively robust to outliers and noise in the data.
Random forests are easy to interpret and can provide insights into the data.
Random forests can be efficiently trained on large datasets.
Disadvantages of Random Forest
Random forests can be computationally expensive to train.
Random forests can be overconfident in their predictions.
Applications of Random Forest
Random forests are used in a wide variety of applications, including:
Classification: Random forests can be used to classify data into two or more categories. For example, they can be used to classify spam emails, diagnose diseases, or predict customer churn.
Regression: Random forests can be used to predict continuous values. For example, they can be used to predict the price of a house, the stock market, or the weather.
Feature selection: Random forests can be used to identify the most important features in a dataset. This can be useful for simplifying models and reducing overfitting.
Anomaly detection: Random forests can be used to detect anomalies in data. This can be useful for fraud detection, intrusion detection, or system monitoring.
Overall, random forests are a powerful and versatile machine learning algorithm that can be used to solve a wide variety of problems.
Decision tree
A decision tree is a supervised learning algorithm that can be used for both classification and regression tasks. It is a tree-like structure that represents a set of decisions and their possible consequences. The tree consists of nodes and branches. Each node represents a decision, and each branch represents the outcome of that decision. The leaves of the tree represent the final decisions.
Decision trees are relatively easy to understand and interpret, even for people who are not familiar with machine learning. This makes them a popular choice for a variety of applications, including:
Medical diagnosis
Fraud detection
Customer churn prediction
Credit risk assessment
Marketing campaign optimization
Decision trees are also relatively efficient to train. This makes them a good choice for large datasets. However, they can be overfitted to the training data, which can lead to poor performance on unseen data. To avoid overfitting, decision trees can be pruned. Pruning is the process of removing nodes from the tree that are not contributing to the overall performance of the model.
One of the benefits of using a decision tree is that it can be easily visualized. This makes it easy to understand how the model is making its decisions. This can be helpful for debugging and for understanding the model's strengths and weaknesses.
Another benefit of using a decision tree is that it is relatively insensitive to outliers. This means that the model is not easily affected by data points that are far away from the rest of the data. This can be helpful for datasets that are noisy or have a lot of outliers.
Overall, decision trees are a powerful and versatile machine learning algorithm that can be used for a variety of tasks. They are relatively easy to understand, interpret, and train. They are also relatively insensitive to outliers. However, they can be overfitted to the training data, which can lead to poor performance on unseen data. To avoid overfitting, decision trees can be pruned.
KNN
K-nearest neighbors (KNN) is a non-parametric, instance-based learning algorithm used for classification and regression. The K in KNN refers to the number of nearest neighbors that will be considered when classifying or predicting the value of a new data point.
How KNN Works
KNN works by measuring the distance between the new data point and all of the data points in the training set. The distance metric used can vary, but the most common is the Euclidean distance. Once the distances have been calculated, the K nearest neighbors are identified. The class or value of the new data point is then determined by majority vote or by taking the average of the classes or values of the K nearest neighbors.
Advantages of KNN
KNN is simple and easy to understand.
KNN is versatile and can be used for both classification and regression problems.
KNN is non-parametric, so it does not make any assumptions about the underlying data distribution.
KNN is relatively insensitive to outliers.
Disadvantages of KNN
KNN can be computationally expensive, especially for large datasets.
KNN is sensitive to the choice of the K value.
KNN can overfit the training data.
Applications of KNN
KNN is a widely used algorithm in a variety of applications, including:
Pattern recognition
Image classification
Recommendation systems
Anomaly detection
Fraud detection
Overall, KNN is a powerful and versatile algorithm that can be used for a variety of machine learning tasks. It is a good choice for problems where the data is well-understood and where there is not too much noise or outliers.
LSTM
Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) architecture used in the field of deep learning. It is particularly well-suited for processing and predicting sequential data, such as time series, text, and speech. Unlike traditional feedforward neural networks, which treat each data point independently, LSTMs maintain a state that is updated with each new input, allowing them to capture long-term dependencies in the data.
Core Components of an LSTM:
Cell State: The cell state serves as the "memory" of the LSTM, persisting over time and carrying information from previous inputs.
Gates: LSTM employs three gates: the forget gate, the input gate, and the output gate. These gates regulate the flow of information into and out of the cell state.
Forget Gate: The forget gate determines which information from the previous cell state should be discarded. It assigns a value between 0 and 1 to each element of the cell state, indicating whether to keep or discard it.
Input Gate: The input gate determines which new information from the current input should be added to the cell state. It also assigns values between 0 and 1, indicating the importance of each input element.
Output Gate: The output gate determines which information from the current cell state should be output to the next layer of the network. It also assigns values between 0 and 1, indicating the importance of each element in the output.
Advantages of LSTMs:
Long-Term Dependency Learning: LSTMs are specifically designed to overcome the vanishing gradient problem, which can hinder the ability of traditional RNNs to learn long-term dependencies.
Sequential Data Handling: LSTMs excel at processing sequential data, effectively capturing patterns and relationships within sequences.
Wide Range of Applications: LSTMs are widely used in various applications, including natural language processing (NLP), machine translation, speech recognition, time series forecasting, and anomaly detection.
Applications of LSTMs:
Natural Language Processing (NLP): LSTMs are used for machine translation, text summarization, sentiment analysis, and chatbot development.
Speech Recognition: LSTMs are employed to convert spoken language into text, enabling voice assistants and speech-to-text applications.
Time Series Forecasting: LSTMs are used to predict future trends in time series data, such as stock prices, weather patterns, and energy consumption.
Anomaly Detection: LSTMs can identify unusual patterns or outliers in sequential data, making them useful for fraud detection and network intrusion detection.
Naive Bayes
Naive Bayes is a simple classification algorithm based on Bayes' theorem, which is a way of calculating probabilities. It is a probabilistic classifier, meaning that it assigns a probability to each possible class label for a given input. Naive Bayes classifiers are among the simplest and most effective classification algorithms, and they are widely used in a variety of applications, including spam filtering, sentiment analysis, and text classification.
The Naive Bayes assumption is that the presence or absence of a particular feature in a class is independent of the presence or absence of any other feature. This is a strong assumption, and in practice, it is often violated. However, despite its simplicity, the Naive Bayes algorithm can be surprisingly effective in practice.
The Naive Bayes classifier works by calculating the probability of each class label given the input data. The input data is typically represented as a vector of features, where each feature is a value that describes some aspect of the input. For example, if the input data is an email message, the features might include the words in the message, the presence of certain punctuation marks, and the sender of the message.
To calculate the probability of each class label, the Naive Bayes classifier uses Bayes' theorem:
P(C | X) = (P(X | C) * P(C)) / P(X)
where:
P(C) is the prior probability of class C
P(X | C) is the likelihood of observing the input data X given that the class is C
P(X) is the prior probability of observing the input data X
The Naive Bayes classifier assumes that the features are independent of each other, so the likelihood of observing the input data X given that the class is C can be calculated by multiplying the probabilities of observing each feature individually.
The Naive Bayes classifier then selects the class label with the highest probability.
Naive Bayes classifiers are very efficient to train and use. They are also relatively insensitive to irrelevant features, which makes them a good choice for problems with high-dimensional data.
However, Naive Bayes classifiers can be susceptible to overfitting, which occurs when the classifier learns the training data too well and is unable to generalize to new data.
Overall, Naive Bayes is a simple and effective classification algorithm that is widely used in a variety of applications.
Here are some of the advantages of Naive Bayes classifiers:
They are simple to understand and implement.
They are efficient to train and use.
They are relatively insensitive to irrelevant features.
Here are some of the disadvantages of Naive Bayes classifiers:
They are based on the assumption that the features are independent of each other, which is often not true.
They can be susceptible to overfitting.
Despite its limitations, Naive Bayes is a powerful and versatile classification algorithm that is well worth considering for a variety of problems.
CNN
Convolutional Neural Networks (CNN) are a type of artificial neural network (ANN) used in machine learning and image recognition. CNNs were inspired by the biological visual cortex, which is the part of the brain responsible for processing visual information. CNNs are particularly well-suited for image recognition tasks because they can learn to identify patterns and features in images that are difficult to detect using traditional methods.
CNN architecture
CNNs work by convolving an input image with a series of filters. Each filter is a small matrix of weights that is applied to a small region of the input image. The output of the convolution is a single value that represents the similarity between the filter and the region of the input image. The filters are then moved across the input image to produce a feature map. The feature map is then passed through a pooling layer, which reduces the dimensionality of the data. This process is repeated for a number of layers, and the output of the final pooling layer is then passed to a fully connected neural network for classification or regression.
CNNs have been used to achieve state-of-the-art results in a variety of image recognition tasks, including object detection, image classification, and facial recognition. CNNs are also being used in other applications, such as natural language processing and speech recognition.
Ensemble technique
Ensemble techniques are machine learning methods that combine multiple models to produce a more accurate and robust predictive model. They are based on the idea that a group of models can collectively perform better than any individual model, especially when the individual models are diverse and have different strengths and weaknesses.
Why use ensemble techniques?
Ensemble techniques offer several advantages over single models:
Improved accuracy: By combining multiple models, ensemble techniques can reduce both bias and variance, leading to more accurate predictions.
Reduced overfitting: Ensemble techniques are less prone to overfitting, which occurs when a model performs well on the training data but poorly on unseen data.
Enhanced robustness: Ensemble techniques are more robust to outliers and noise in the data.
Types of ensemble techniques
There are several different types of ensemble techniques, each with its own strengths and weaknesses. Some of the most common types include:
Bagging (bootstrap aggregating): Bagging trains multiple models on different subsets of the training data. The final prediction is made by averaging the predictions of all the models.
Boosting: Boosting trains multiple models sequentially, where each model attempts to correct the errors of the previous model. The final prediction is made by combining the predictions of all the models.
Stacking: Stacking trains a meta-model on the predictions of multiple base models. The meta-model learns to combine the predictions of the base models in a way that minimizes overall error.
Applications of ensemble techniques
Ensemble techniques are widely used in a variety of machine learning applications, including:
Classification: Predicting a categorical outcome, such as whether an email is spam or not.
Regression: Predicting a continuous outcome, such as the price of a house.
Anomaly detection: Identifying unusual data points that may indicate fraud or other problems.
Examples of ensemble techniques in use
Ensemble techniques have been used to achieve state-of-the-art results in a variety of competitions, such as the Netflix Prize and the Kaggle competitions. They are also widely used in practice by companies such as Google, Microsoft, and Amazon.
Conclusion
Ensemble techniques are a powerful and versatile tool for improving the performance of machine learning models. They are a valuable addition to any machine learning practitioner's toolkit.
Reinforcement learning (RL)
Reinforcement learning (RL) is a type of machine learning that enables an agent to learn how to make optimal decisions in an environment through trial and error. It is well-suited for recommendation systems because it can handle sequential, dynamic user-system interactions and take into account long-term user engagement.
How Reinforcement Learning Works for Recommendation Systems
In the context of recommendation systems, RL can be formulated as a Markov decision process (MDP), which is a mathematical framework for modeling sequential decision-making problems. In an MDP, there are four key components:
States: These are the possible configurations of the system at any given time. In a recommendation system, the state might include the user's current context, such as their location, time of day, and past interactions with the system.
Actions: These are the decisions that the agent can make. In a recommendation system, an action might be recommending a particular item to a user.
Rewards: These are the values that the agent receives for taking actions. In a recommendation system, a reward might be positive if the user clicks on the recommended item or purchases it, and negative if they ignore it.
Transition probabilities: These are the probabilities of moving from one state to another after taking an action. In a recommendation system, transition probabilities would reflect the likelihood of a user engaging with a recommended item.
The goal of RL is to learn a policy, which is a mapping from states to actions, that maximizes the expected long-term reward. This is achieved through trial and error, where the agent interacts with the environment, receives rewards, and updates its policy accordingly.
Benefits of Using Reinforcement Learning for Recommendation Systems
RL offers several advantages over traditional recommendation methods, such as collaborative filtering and content-based filtering:
Adaptive and personalized recommendations: RL can continuously learn and adapt to individual user preferences, providing more relevant and personalized recommendations over time.
Exploration and exploitation: RL can balance the exploration of new items with the exploitation of known items, ensuring that users are exposed to both familiar and novel content.
Long-term optimization: RL can take into account long-term user engagement, aiming to maximize user satisfaction over extended periods rather than just immediate clicks or purchases.
Challenges of Using Reinforcement Learning for Recommendation Systems
Despite its advantages, RL also faces some challenges in the context of recommendation systems:
Data sparsity and cold start problems: RL algorithms require large amounts of data to learn effective policies, which can be difficult to obtain in some recommendation scenarios.
Delayed rewards: User engagement signals, such as clicks or purchases, can be delayed, making it difficult for RL algorithms to attribute rewards directly to specific actions.
Exploration-exploitation trade-off: Balancing exploration and exploitation is crucial for RL algorithms, as excessive exploration can lead to suboptimal recommendations, while excessive exploitation can prevent the discovery of new and valuable items.
Applications of Reinforcement Learning for Recommendation Systems
RL is being applied in a variety of recommendation systems, including:
E-commerce: RL is used to recommend products to users based on their browsing history and purchase patterns.
News feeds: RL is used to personalize news articles and other content based on user interests and past interactions.
Streaming services: RL is used to recommend movies, TV shows, and music to users based on their viewing habits and preferences.
Conversational recommendation: RL is used to guide conversations with users, recommending items or services based on the context of the conversation.
As RL algorithms continue to develop and improve, we can expect to see even more innovative applications in the field of recommendation systems.
Natural language processing (NLP)
Natural language processing (NLP) is an interdisciplinary field that studies the interaction between computers and human language. It encompasses a wide range of tasks, including:
Natural language understanding (NLU): The ability of computers to understand the meaning of human language. This includes tasks such as machine translation, text summarization, and sentiment analysis.
Natural language generation (NLG): The ability of computers to generate human-like text. This includes tasks such as chatbots, email generation, and creative writing.
Speech recognition: The ability of computers to transcribe spoken language into text.
Speech synthesis: The ability of computers to generate spoken language from text.
NLP is used in a wide variety of applications, including:
Machine translation: NLP is used to translate text from one language to another. For example, Google Translate uses NLP to translate text between over 100 languages.
Text summarization: NLP is used to summarize text documents. For example, Google Search uses NLP to summarize web pages.
Sentiment analysis: NLP is used to analyze the sentiment of text data. For example, social media companies use NLP to analyze customer sentiment about their products.
Chatbots: NLP is used to create chatbots that can interact with humans in a natural way. For example, customer service chatbots use NLP to answer customer questions.
Email generation: NLP is used to generate emails. For example, marketing companies use NLP to generate personalized email campaigns.
Creative writing: NLP is used to generate creative text formats, such as poems, code, scripts, musical pieces, email, letters, etc.
NLP is a rapidly growing field of research, and there is a lot of potential for new applications in the future. As NLP technology continues to develop, we can expect to see even more ways that computers can interact with humans in a natural and meaningful way.
Recommendation System
A recommendation system is a subclass of information filtering system that uses algorithms and data to recommend relevant items to users. These systems are widely used in various applications, including e-commerce, streaming services, and social media platforms.
Types of Recommendation Systems
There are two main types of recommendation systems:
Content-based filtering: This approach recommends items to users based on their similarity to items they have liked or interacted with in the past. For example, an e-commerce site might recommend products to a user based on their browsing history or purchase history.
Collaborative filtering: This approach recommends items to users based on the preferences of other similar users. For example, a streaming service might recommend movies or TV shows to a user based on the preferences of other users who have similar viewing habits.
Benefits of Recommendation Systems
Recommendation systems provide several benefits, including:
Personalized user experience: By recommending items that are relevant to each user's individual preferences, recommendation systems can help to create a more personalized and engaging user experience.
Increased sales and revenue: By helping users to discover new products and services, recommendation systems can help to increase sales and revenue for businesses.
Reduced decision fatigue: By narrowing down the overwhelming number of choices available to users, recommendation systems can help to reduce decision fatigue and make it easier for users to find what they are looking for.
Examples of Recommendation Systems
Some of the most well-known examples of recommendation systems include:
Amazon's "Customers who bought this item also bought" recommendations
Netflix's "Recommended for you" suggestions
YouTube's "Up next" videos
Spotify's "Discover Weekly" playlists
Facebook's "Friends who like this" recommendations
MLP
A multilayer perceptron (MLP) is a type of feedforward artificial neural network (ANN) that is composed of multiple layers of perceptrons, or neurons. Each perceptron is a simple processing unit that takes a weighted sum of its inputs and applies an activation function to produce an output. The weights of the connections between the perceptrons are adjusted during training using an algorithm called backpropagation.
MLPs are universal approximators, meaning that they can be trained to approximate any continuous function to arbitrary precision. This makes them a powerful tool for a variety of tasks, including classification, regression, and pattern recognition.
MLPs have been used successfully in a wide range of applications, including:
Image recognition
Speech recognition
Natural language processing
Machine translation
Medical diagnosis
Financial fraud detection
Recommender systems
MLPs are a relatively simple type of ANN, but they can be very effective for a wide range of tasks. They are a good starting point for beginners who are learning about ANNs.
Here are some of the advantages of MLPs:
They are relatively easy to understand and implement.
They are very efficient to train.
They are able to learn complex relationships between input and output data.
Here are some of the disadvantages of MLPs:
They can be sensitive to the choice of hyperparameters.
They can be overfitted to training data.
They can have difficulty learning long-range dependencies in data.
Overall, MLPs are a powerful and versatile tool for machine learning. They are a good choice for a wide range of tasks, and they are a good starting point for beginners.
J-48 algorithm
In machine learning, J48 is a widely used decision tree classification algorithm. It is an extension of the ID3 (Iterative Dichotomiser 3) algorithm and is implemented in the WEKA data mining tool. J48 is known for its efficiency and accuracy in building decision trees for classification tasks.
Here are some of the key features of the J48 algorithm:
Information gain: J48 uses information gain to split the data into subsets at each node of the decision tree. Information gain measures the reduction in uncertainty after a split is made.
Pruning: J48 employs pruning techniques to prevent overfitting, which can occur when the decision tree becomes too complex and memorizes the training data too closely.
Handling missing values: J48 can handle missing values in the data by using alternative splitting strategies or imputing missing values.
Continuous attribute discretization: J48 can discretize continuous attributes into intervals to make them suitable for decision tree construction.
Rule generation: J48 can generate classification rules from the decision tree, which provides a more interpretable representation of the model.
J48 is a versatile algorithm that can be applied to a wide range of classification problems, including:
Medical diagnosis
Customer segmentation
Risk assessment
Fraud detection
Image classification
Overall, the J48 algorithm is a powerful and popular tool for machine learning classification tasks. Its ability to handle missing values, discretize continuous attributes, and generate interpretable rules makes it a valuable choice for many applications.
Gradient descent
Gradient descent is an optimization algorithm commonly used in machine learning to find the minimum of a function. It is an iterative process that involves repeatedly moving in the direction of the steepest descent of the function until a minimum is reached. In machine learning, gradient descent is used to train models by minimizing the error between the model's predictions and the actual data.
Here is a simplified explanation of how gradient descent works:
Start with an initial set of parameters. These parameters could be the weights and biases of a neural network, or the coefficients of a linear regression model.
Calculate the gradient of the cost function with respect to the parameters. The gradient is a vector that points in the direction of the steepest descent of the cost function.
Update the parameters by moving in the direction of the negative gradient. This means subtracting a small multiple of the gradient from each parameter.
Repeat steps 2 and 3 until the cost function is minimized.
Gradient descent is a powerful tool for training machine learning models, but it can also be sensitive to the choice of learning rate and other hyperparameters. If the learning rate is too small, the algorithm will take too long to converge to a minimum. If the learning rate is too large, the algorithm may overshoot the minimum and oscillate around it.
Here are some of the advantages of gradient descent:
It is a relatively simple algorithm to implement.
It is very effective for finding local minima of many different types of functions.
It is well-studied and there are many different variations of the algorithm that can be used to improve its performance.
Here are some of the disadvantages of gradient descent:
It can be sensitive to the choice of learning rate and other hyperparameters.
It can get stuck in local minima and not reach the global minimum of the function.
It can be slow to converge for some types of functions.
Despite its limitations, gradient descent is a valuable tool for machine learning and is widely used in practice.
TF-IDF
TF-IDF stands for term frequency-inverse document frequency. It is a statistical measure that evaluates how relevant a word is to a document in a collection of documents. The importance of a word is calculated by multiplying two values:
Term frequency (TF): This is the number of times a word appears in a document. The more times a word appears, the more important it is likely to be.
Inverse document frequency (IDF): This is a measure of how common or rare a word is across the entire collection of documents. The more common a word is, the less important it is likely to be. This is because common words are not very distinctive and do not provide much information about the content of a document.
The TF-IDF score is calculated as follows:
TF-IDF = TF * IDF
A high TF-IDF score indicates that a word is both frequent in a document and rare across the collection of documents. This suggests that the word is a good keyword for the document and that it is likely to be relevant to a search query.
TF-IDF is a popular weighting scheme in information retrieval and text mining. It is used in a variety of applications, including:
Search engines: TF-IDF is used to rank search results so that the most relevant results are returned first.
Document classification: TF-IDF can be used to classify documents into different categories.
Recommendation systems: TF-IDF can be used to recommend documents that are similar to a user's current document.
Here is an example of how TF-IDF is used to rank search results:
Suppose a user searches for the term "artificial intelligence." The search engine will use TF-IDF to calculate the relevance of each document in the collection to the search query. The documents with the highest TF-IDF scores will be ranked at the top of the search results.
TF-IDF is a simple and effective weighting scheme that can be used to improve the performance of a variety of text-based applications.