How do you choose the right algorithm for a machine learning problem?

Najwa

What factors do you consider when selecting the most suitable machine learning algorithm for a specific problem?

Arun_Mishra

Picking the right machine learning tool is like choosing the right tool for a job. First, know what you're trying to do—like sorting things, making predictions, or grouping stuff together. If you don't have much data, use simpler tools so you don't get confused. But if you have lots of data, you can try fancier tools.

Check if your info is numbers or categories, as some tools like one type more. Think about whether things are kind of straight or all twisty-turny. If your groups are uneven, some tools handle that better. And if you want to understand how the tool is making decisions, simpler tools are clearer.

Consider how fast and strong your computer is—some tools need a lot of power. Mixing a bunch of tools together can be even better than using just one. Ask people who know a lot about the stuff you're working on for advice. Try different tools and see which one works best. And keep an eye out for new tools—sometimes there are even better ones coming out .

Caleb

I believe there is always learning curve!!

Step 1: By Understanding your data and its properties can help you choose the algorithm that will be most useful in building your model.

For example:

Regression algorithms can be used to analyze time-series data.

Classification algorithms can be used to analyze discrete data.

Association algorithms can be used for data with correlated attributes.

Step 2: CHOOSING AN ALGORITHM

For customer segmentation and/or community detection in the social sphere, you’d need clustering algorithms.

For customer retention or to develop a recommended system, you’d use classification algorithms.

For credit scoring or predicting the next outcome of time-driven events, you’d use a regression algorithm.

Example of classification algorithm

When your data is divided into clusters of similar objects, your model is in a better position to make reliable predictions

DECISION TREES-A decision tree is an approach to analysis that can help you make decisions. Suppose, for example, that you need to decide whether to identify a certain count of people in one of three groups: age, family history, or a gender. Please work out the rate of positive or failure for each of these groups as percentages. Using such a decision tree to decide on a group begins with calculating the expected value for each alternative — a numbered rank that helps you select the correct one.

SUPPORT VECTOR MACHINE-Algorithm that assigns new data elements to one of labeled categories. SVM is, in most cases, a binary classifier; it assumes that the data in question contains two possible target values.

Example: Suppose you’re designing a predictive analytics model that will automatically recognize and predict the name of an object in a picture.

NAIVE BAYES CLASSIFICATION ALGORITHM-Data-classification algorithm that is based on probabilistic analysis and Bayes’ Theorem, powerful formula that can be used to calculate the probability of an event based on evidence.

LINEAR REGRESSION

Linear regression is a statistical method that analyzes and finds relationships between two variables. Consider an example of data that contains two variables: past data consisting of the arrival times of a flight and its corresponding delay time. Suppose you want to predict what the delay would be for the next flight. If you apply linear regression to these two variables — the arrival and delay times — you can generate a linear equation such as

Delay = a + (b * Arrival time) + d

The constants a and b are the model’s parameters. The variable d is the error term (also known as the remainder) — a numerical value that represents the mismatch between the two variables delay and arrival time. If the error is not equal to zero, then that might indicate that there are criteria affecting the variable delay.

Note : Sometimes based on the result, need to look into the other algorithms.I believe there is always learning curve!!

Michael_Charton

Start simple to get a sense of how hard the problem is. This usually involves running something off-the-shelf, for instance, logistic regression or SVM for classification problems. If these algorithms do fairly well, then the problem is simple, and pretty much every algorithm will work reasonably well.
If you do not get a satisfactory solution, then the problem might be harder, and you may have to use more advanced techniques. These would vary from problem to problem. For instance, a classification problem may be highly non-linear. So, you may need to do some non-trivial feature engineering for the simpler algorithms to work, use custom kernel functions for algorithms like SVM, or use deep learning for feature engineering (assuming you have sufficient data).

Another kind of hardness could be that the problem has too many features and too few training samples. In that case, you would mostly want to do some sort of feature selection, using techniques like sparse regression models (e.g. LASSO) or PCA.

Finally, a lot of this comes from experience. An expert would usually know in what order to try various algorithms for a specific kind of problem, such that you are roughly going from the simpler techniques to the more complicated ones. You would want to stop at a point where making the algorithm more complex doesn't give you much improvement in performance.

Yath_Arth

After having a look at dataset, some typical exploratory data analysis and preprocessing steps, I start with the simplest algorithm (for example: regression or SVM). I split the dataset into train and test set, apply ML algorithms on it and check the accuracies/RMSEs through repeated cross validation/k-fold cross validation. Then, finally I try to perform hyperparameter optimization though grid search or random search and improve the model to perform better on new datasets.

However, there are some “thumb rules” to apply ML algorithms on datasets. For example: if I am into hurry and want good results without a lot of analysis, I use random forest or gradient boosting since they always perform pretty well on all kind of datasets. I never use Naive Bayes until the features seem decorrelated to me. I rarely use SVM for very high dimensional datasets.

There are few blogs and even research papers that discuss on how to choose ML algorithms but I found my own way quite satisfactory and intuitive.

Dave_Lawn

Let us try to put it in a simple way.

If you try to think about it, an algorithm is nothing but a collection of decisions taken one after the other in order to achieve a particular task/target. Through machine learning, we are trying to make machines learn which set of decisions to make while trying to tackle a particular problem or while trying to reach a particular target. This is what humans and animals do every day, at every moment in their lives. The fact that you are reading this answer can be explained in terms of algorithms:

You decided to know why algorithms are important for machine learning (this is your target)
You searched on Google or opened Quora to search for the same (this is because you’ve learned with experience (or trial and error) that you can find answers through this process easily).
So you are trying to make a choice which leads you to your target quickly.
This is exactly why algorithms are important for machine learning. When we are trying to train a machine to perform a task, we are providing it with a set of instructions (the algorithm) to reach the target quickly.

In some cases, the machine might need to be explicitly told the path to follow (for example, Supervised Learning). The analogy here can be a friend telling you that you can find/ask questions on Quora. In other cases, you might need to figure it out yourselves. For example, you just have access to the internet and nothing else. You reach an answer on Quora by trial and error (this is the case similar to Unsupervised Learning).

Through appropriate algorithms, a machine can take the advantage of making the learning process faster and efficient. For instance, if we don't have algorithms which can take advantage of the hardware (GPUs) in our machines, we won’t be able to make the learning process faster. Tony Petrov’s answer reflects more on this and the scaling aspect!

I hope this was helpful in understanding the basic need of algorithms not only in machine learning but in any domain!

Sameer_Charde

Artificial intelligence (AI) is revolutionizing various industries by enhancing efficiency, improving decision-making, and enabling new capabilities. Here are some key ways AI is transforming different sectors:

Healthcare

Diagnosis and Treatment: AI algorithms analyze medical images and data to assist in diagnosing diseases like cancer and predicting patient outcomes.
Personalized Medicine: AI helps tailor treatments to individual patients based on their genetic information and other personal data.
Operational Efficiency: AI optimizes hospital operations, from scheduling staff to managing supply chains.

Finance

Fraud Detection: AI systems analyze transaction patterns to detect and prevent fraudulent activities.
Algorithmic Trading: AI-driven trading algorithms make investment decisions faster and more accurately than human traders.
Customer Service: Chatbots and virtual assistants provide 24/7 customer support, handling inquiries and transactions.

Retail

Personalized Shopping: AI analyzes customer data to provide personalized product recommendations and targeted marketing.
Inventory Management: AI predicts demand, optimizing inventory levels and reducing waste.
Automated Customer Service: AI-powered chatbots assist customers, improving the shopping experience.

Manufacturing

Predictive Maintenance: AI predicts equipment failures before they occur, reducing downtime and maintenance costs.
Quality Control: AI systems inspect products for defects more accurately than human inspectors.
Supply Chain Optimization: AI optimizes supply chains by predicting demand and managing logistics.

Transportation

Autonomous Vehicles: AI powers self-driving cars, improving safety and efficiency in transportation.
Traffic Management: AI systems analyze traffic patterns and optimize traffic flow, reducing congestion.
Logistics: AI optimizes route planning for delivery services, reducing costs and improving delivery times.

Energy

Smart Grids: AI optimizes energy distribution and consumption, improving the efficiency of power grids.
Renewable Energy: AI predicts weather patterns to optimize the performance of solar and wind energy systems.
Energy Management: AI systems help manage energy use in buildings, reducing costs and environmental impact.

Entertainment

Content Creation: AI assists in creating music, writing scripts, and generating visual content.
Personalized Recommendations: AI recommends movies, music, and other content based on user preferences.
Interactive Experiences: AI powers interactive experiences in video games and virtual reality.

Education

Personalized Learning: AI customizes learning experiences based on individual student needs and progress.
Administrative Efficiency: AI streamlines administrative tasks, such as grading and scheduling.
Tutoring: AI-driven tutoring systems provide additional support to students outside the classroom.

Agriculture

Precision Farming: AI analyzes data from sensors and drones to optimize crop yields and reduce resource usage.
Disease Detection: AI identifies plant diseases early, enabling timely intervention.
Supply Chain Optimization: AI improves the efficiency of the agricultural supply chain from farm to table.

Legal

Document Review: AI speeds up the review of legal documents and contracts.
Predictive Analytics: AI predicts case outcomes and assists in legal research.
Compliance: AI helps ensure compliance with regulations by monitoring and analyzing data.
In summary, AI is transforming industries by automating tasks, providing insights from data, and enabling innovative solutions that improve efficiency, accuracy, and overall effectiveness.

Joni_Clark_Stellar

I’ll focus on healthcare as that is my preferred industry. Here are a few specific examples:

Clinical trial matching:
problem: the ratio of patients to people with the appropriate knowledge to determine who to include in / recruit to a clinical trial (the doctors running it) is too high and much of the data that determines a patient’s eligibility is in raw text format in patient notes instead of in an easily searchable database. This causes many people who could potentially benefit from a new treatment to miss the opportunity.
solution: vectorize (turn into a set of numbers that each have some semantic meaning) the eligibility and disqualification criteria so that we can use AI to do a first pass that eliminates the patients that can confidently be rolled out. The rest are then presented to the clinicians along with links to the found evidence of eligibility to determine which ones to include in the trial. A couple papers I recently read that do something like this are https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7959675/ (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7959675/) and DeepEnroll: Patient-Trial Matching with Deep Embedding and Entailment Prediction (DeepEnroll: Patient-Trial Matching with Deep Embedding and Entailment Prediction)
Top ethical concerns:
as with any health-related data, deidentification of PHI like names, dates, places, etc. (AI often helps here too) and we must treat data used as opt-in only
making sure not to eliminate any eligible matches. Always error on the side of inclusion in the potential matches that are shown to the physicians
Using audio and natural language processing to speed up note writing:
problem: Physicians spend much of their time writing notes. This detracts from some combination of the number of patients seen per doctor and the time they can spend on each patient.
solution: Use speech recognition, voice separation in conversational audio, and information extraction techniques along with note templates to automatically write the initial note for a patient based on what is said in the room then present it to the doctor for review later.
Top ethical concerns:
as always, privacy, opting in, etc.
controlling the tradeoff between filtering and inclusion of information. I would say the best tradeoff is not the same for every situation or every physician so it’s probably best to allow the physician to specify a value between 0 and 1 to signify the amount of detail to be included in each initial report
Using AI to detect a potential oncoming manic or depressive episode or for suicidal ideation detection based on the patient’s phone usage / mood journal data and then alerting some combination of the patient, physician and counselor to enact preventative measures
Top ethical concerns:
as always, privacy, opting in, etc.
Accuracy and explainability here are also hugely important here because you don’t want to i.e. misidentify the patient as suicidal if they're just sad because that could cause further decline in the patient’s mental condition (imagine just feeling really sad for a few days because a friend killed themself and then seeing a notification on your phone that says “are you suicidal? help is available” - that could be quite anxiety-producing to say the least) but this also has to be balanced with the fact that we don’t want anybody to slip through the cracks. Again, a good solution here is to allow the patient and their medical team to determine a “sensitivity” value that provides clear examples of what would trigger each type of value (this can be done in a number of different ways including use of the classifying model itself to generate examples).