Last updated on Oct 30, 2023
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Logistic regression
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Naive Bayes
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K-nearest neighbors
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Decision trees
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Random forests
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Neural networks
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Here’s what else to consider
Machine learning is a branch of artificial intelligence that enables computers to learn from data and make predictions. One of the most common tasks in machine learning is to predict future probabilities, such as the likelihood of a customer buying a product, a patient developing a disease, or a stock price rising or falling. To do this, machine learning algorithms use various techniques to find patterns, optimize parameters, and evaluate outcomes. In this article, we will explore some of the top machine learning algorithms used to predict future probabilities, and how they work.
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- Syeda Maria Hussain Senior Consultant Data Science @ Systems Limited | Machine Learning Engineer |MLOps
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- Abdelkhalek Bakkari CEO and Founder of Caustaza Group Ltd | PhD in Computer Science | Top Artificial Intelligence Voice on LinkedIn |…
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- Andrew Woon Managing Director at Faber-Castell Malaysia, Indochina, East Asia (Japan, South Korea, Taiwan, Philippines)
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1 Logistic regression
Logistic regression is a simple but powerful algorithm that models the relationship between a set of features and a binary outcome. It uses a logistic function to map the input values to a probability between 0 and 1, and then assigns a class label based on a threshold. For example, if the probability is greater than 0.5, the label is 1, otherwise it is 0. Logistic regression can be used to predict future probabilities of events such as email spam, credit default, or cancer diagnosis.
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- Syeda Maria Hussain Senior Consultant Data Science @ Systems Limited | Machine Learning Engineer |MLOps
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In my opinion, Decision Tree based ensemble learning algorithms like Random Forest or XGboost are good options due to the following reasons:- Estimates likelihood of the outcome- Can work for classification as well as regression problems- Can be used for feature importance- Robust to outliers and can handle missing values in the data- Works well with limited amount of data as compared to deep learning algorithms
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- Erfan Khalaji Artificial Intelligence Developer
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Logistic Regression: estimates the probability of an instance belonging to a particular class.Naive Bayes: calculates the probability of a document belonging to a particular category.Decision Trees: used for both classification and regression tasks, and they can provide probabilities for each class by counting instances in leaf nodes.Gradient Boosting: used for predictive modeling and offer probability estimates for classification tasks.Neural Networks: can provide probability distributions over multiple classes, making them suitable for various prediction tasks.Ensemble Methods: improves the accuracy of probability predictions by combining the outputs of multiple models.
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- Abdelkhalek Bakkari CEO and Founder of Caustaza Group Ltd | PhD in Computer Science | Top Artificial Intelligence Voice on LinkedIn | Top Web Applications Voice on LinkedIn | Top Algorithms Voice on LinkedIn
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One thing I have found very helpful is that logistic regression is a versatile algorithm with a straightforward yet powerful approach. It's particularly beneficial for situations where we need to predict binary outcomes or probabilities, making it a valuable tool in data analysis and machine learning. The use of the logistic function to transform input values into probabilities simplifies the process, and the ability to set a threshold for classification adds flexibility. This algorithm has widespread applications, from identifying email spam to assessing credit risk and even aiding in medical diagnoses. Its simplicity and effectiveness make it a fundamental technique in predictive modelling.
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2 Naive Bayes
Naive Bayes is a fast and easy algorithm that applies the Bayes' theorem to calculate the conditional probabilities of each class given the features. It assumes that the features are independent of each other, which is often not true in real life, but still works well in many cases. Naive Bayes can handle both discrete and continuous features, and can deal with missing data. It can be used to predict future probabilities of categories such as sentiment analysis, text classification, or spam detection.
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- Terrance Ford Senior Backend Engineer at ScreenMeet
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If you know the features are dependent on each other, then you can use Bayes Networks to model the conditionally independent variables, and their relationships. Once you have this, you can answer conditional probability questions, like "What is the probability of class A, given evidence D", by solving for exact probability distributions, using solver optimization techniques like "Variable Elimination." If the number of variables is prohibitively large, you can estimate the probability using Monte Carlo methods like Gibbs Sampling, or the Metropolis Hastings algorithm.
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- Abdelkhalek Bakkari CEO and Founder of Caustaza Group Ltd | PhD in Computer Science | Top Artificial Intelligence Voice on LinkedIn | Top Web Applications Voice on LinkedIn | Top Algorithms Voice on LinkedIn
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An aspect I've particularly appreciated that Naive Bayes is a quick and straightforward algorithm that leverages Bayes' theorem to compute conditional probabilities for each class based on the given features. While it makes the simplifying assumption of feature independence, which may not hold in reality, Naive Bayes surprisingly performs well in various applications. Its versatility in handling both discrete and continuous features, as well as its ability to manage missing data, makes it a valuable choice. Naive Bayes finds utility in predicting probabilities for categories, such as sentiment analysis, text classification, or spam detection. Despite its simplicity, it can yield impressive results in practical scenarios
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3 K-nearest neighbors
K-nearest neighbors is a simple but effective algorithm that predicts the class of a new instance based on the majority vote of its k closest neighbors in the feature space. It does not require any training or parameter tuning, but only stores the data and calculates the distances between instances. It can handle both numerical and categorical features, and can be adapted to different distance metrics and weighting schemes. It can be used to predict future probabilities of clusters such as customer segmentation, image recognition, or anomaly detection.
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- Abdelkhalek Bakkari CEO and Founder of Caustaza Group Ltd | PhD in Computer Science | Top Artificial Intelligence Voice on LinkedIn | Top Web Applications Voice on LinkedIn | Top Algorithms Voice on LinkedIn
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The paragraph provides a clear and accurate overview of the K-nearest neighbours algorithm. It correctly highlights its simplicity, adaptability to various data types, and absence of a training phase. However, it could further mention KNN's sensitivity to the choice of K and its performance limitations in high-dimensional data spaces. Despite this, it effectively communicates the algorithm's practicality and versatility in real-world applications.
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4 Decision trees
Decision trees are intuitive and flexible algorithms that build a tree-like structure of rules to split the data into hom*ogeneous groups based on the features. Each node in the tree represents a feature, each branch represents a condition, and each leaf represents a class. Decision trees can handle both numerical and categorical features, and can perform feature selection and handle missing data. They can be used to predict future probabilities of outcomes such as loan approval, fraud detection, or medical diagnosis.
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- Abid Ali Awan Data Scientist | Technical Writer | Editor
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Key points about Decision tree algorithms:- Estimates likelihood of outcomes- Manages both numerical and categorical features efficiently.- Reveals significant features through early tree splits and effective data grouping.- Provides a visually intuitive tree structure for understanding decision-making.- Forms the basis for advanced ensemble methods like Random Forests and Gradient Boosting Machines.- Contains mechanisms to handle missing data, ensuring accuracy in real-world datasets.
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5 Random forests
Random forests are powerful and robust algorithms that combine multiple decision trees to create an ensemble of predictions. They use two techniques to reduce the variance and overfitting of individual trees: bootstrap sampling and feature bagging. Bootstrap sampling means that each tree is trained on a random subset of the data, and feature bagging means that each split is based on a random subset of the features. Random forests can handle both numerical and categorical features, and can perform feature importance and handle missing data. They can be used to predict future probabilities of events such as customer churn, stock market movement, or disease progression.
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- Mohit Joshi Director of Data and ML Platforms | Top Artificial Intelligence Voice | Top Data Engineering Voice | Data Leader | Data Products Spokesperson | Building Teams | Speaker | Solution Architect | AI Expert | Mentor
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The Random Forest algorithm, a versatile and robust ensemble method, can be likened to a "wise council" that combines the insights of multiple experts, much like it amalgamates the predictions of numerous decision trees, making it effective in tasks such as predicting customer behavior, where it leverages diverse inputs to provide recommendations, and in ecological modeling, where it merges varied data sources to make informed environmental predictions. In these diverse applications, the Random Forest algorithm harnesses the collective output of its constituent models, demonstrating its role as a wise and dependable decision-making tool in the realm of machine learning and data analysis.
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- Abid Ali Awan Data Scientist | Technical Writer | Editor
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Key advantages of using Random forest:- Combines multiple decision trees to produce more accurate and stable predictions.- Versatile in handling both classification and regression tasks efficiently.- Mitigates overfitting common in single decision trees, delivering robust performance on unseen data.- Enables concurrent tree building, expediting the training process.- Often delivers superior accuracy compared to individual decision trees, especially on large datasets.- Capable of handling missing data by either imputation or surrogate splits.
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6 Neural networks
Neural networks are complex and versatile algorithms that mimic the structure and function of the human brain. They consist of layers of interconnected nodes that process and transmit information through activation functions and weights. Neural networks can learn nonlinear and high-dimensional relationships between the features and the target, and can handle both numerical and categorical features. They require a lot of data and computational resources, and can be prone to overfitting and local minima. They can be used to predict future probabilities of tasks such as natural language processing, computer vision, or speech recognition.
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- Abid Ali Awan Data Scientist | Technical Writer | Editor
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Neural networks are capable of capturing complex non-linear relationships between features and targets. It uses backpropagation for efficient error minimization and weight adjustment during training. Moreover, it uses various activation functions to introduce non-linearity and control the neuron's output. Neural networks incorporate techniques like dropout, regularization to mitigate overfitting. It automatically learns feature representations, reducing the need for manual feature engineering.
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7 Here’s what else to consider
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- Andrew Woon Managing Director at Faber-Castell Malaysia, Indochina, East Asia (Japan, South Korea, Taiwan, Philippines)
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For tabular numerical data, XGBoost is extremely accurate. If you need faster training speed, go for LightGBM. And for tabular categorical data CatBoost produces the higher accuracy vs XGBoost.For Time Series prediction with few predictors, Prophet is fast and has high accuracy. For multi-variate time series, its better to use AutoML like H2O or AutoGluon which tests on multiple algorithms as well as ensemble for highest accuracy.
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- Mohit Joshi Director of Data and ML Platforms | Top Artificial Intelligence Voice | Top Data Engineering Voice | Data Leader | Data Products Spokesperson | Building Teams | Speaker | Solution Architect | AI Expert | Mentor
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Transformer Models, particularly exemplified by the GPT and BERT architectures, have revolutionized natural language processing, driving advancements in areas like chatbots and sentiment analysis. XGBoost is a highly efficient and scalable algorithm for structured/tabular data tasks, finding applications in financial services and competitive data science. Generative Adversarial Networks (GANs), on the other hand, are reshaping creativity and realism, being employed for image synthesis, video generation, and AI-generated art, marking the forefront of machine learning's cutting-edge developments across multiple domains.
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- Syahrizal S. PhD (Student) | MSc (Math) | BE (EE) | ESG (BEV Charging Behavior) | Applied AI (Manufacturing) | HVAC Analysis | CMOS Analog Design | Cat 2 Vibration (Mobius Inst.) | PRINCE2 | Innovation (GIM Inst.) | P. Tech
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I prefer the Box-Jenkins model for time-series data with clear trends and seasonality. I have done BEV charging behavior analysis using the model and found that the error percentage is within the tolerable limit.
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