Mlearning.ai

NOVEMBER 30, 2023

Mastering Tree-Based Models in Machine Learning: A Practical Guide to Decision Trees, Random Forests, and GBMs Image created by the author on Canva Ever wondered how machines make complex decisions? Just like a tree branches out, tree-based models in machine learning do something similar. So buckle up!

Machine Learning 52

Machine Learning Machine Learning Decision Trees Data Science 52

DrivenData Labs

MAY 24, 2023

Several additional approaches were attempted but deprioritized or entirely eliminated from the final workflow due to lack of positive impact on the validation MAE. Summary of approach: Our solution for Phase 1 is a gradient boosted decision tree approach with a lot of feature engineering.

Machine Learning 225

Machine Learning Machine Learning Data Science Decision Trees 225

DrivenData Labs

MAY 22, 2024

There are two model architectures underlying the solution, both based on the Catboost implementation of gradient boosting on decision trees. Final Prize Stage : Refined models are being evaluated once again on historical data but using a more robust cross-validation procedure.

Cross Validation 130

Cross Validation Machine Learning Machine Learning ML 130

Ocean Protocol

NOVEMBER 28, 2024

2nd Place: Yuichiro “Firepig” [Japan] Firepig created a three-step model that used decision trees, linear regression, and random forests to predict tire strategies, laps per stint, and average lap times. Firepig refined predictions using detailed feature engineering and cross-validation.

Cross Validation 85

Cross Validation Decision Trees Data Scientist Data Science 85

DagsHub

JUNE 3, 2024

This can be done by training machine learning algorithms such as logistic regression, decision trees, random forests, and support vector machines on a dataset containing categorical outputs. So, if you have a large number of features but fewer samples, consider using an algorithm like a decision tree or a linear model.

Machine Learning 52

Machine Learning Machine Learning Decision Trees ML 52

Mlearning.ai

FEBRUARY 15, 2024

This cross-validation results shows without regularization. Decision Tree This will create a predictive model based on simple if-else decisions. So far, the Decision tree classifier model with max_depth =10 and the min_sample_split = 0.005 has given the best result. Why am I using regularization?

Data Science 52

Data Science Decision Trees Exploratory Data Analysis ML 52

Smart Data Collective

JULY 1, 2023

Provide examples and decision trees to guide annotators through complex scenarios. Cross-validation Divide the dataset into smaller batches for large projects and have different annotators work on each batch independently. Then, cross-validate their annotations to identify discrepancies and rectify them.

Cross Validation 64

Cross Validation AI AI Machine Learning 64

Pickl AI

JANUARY 8, 2025

Tree-Based Methods Decision trees and ensemble methods like Random Forest and Gradient Boosting inherently perform feature selection. Here, we discuss two critical aspects: the impact on model accuracy and the use of cross-validation for comparison.

Machine Learning 52

Machine Learning Machine Learning Cross Validation Support Vector Machines 52

Pickl AI

JULY 26, 2023

Here are some examples of variance in machine learning: Overfitting in Decision Trees Decision trees can exhibit high variance if they are allowed to grow too deep, capturing noise and outliers in the training data. Regular cross-validation and model evaluation are essential to maintain this equilibrium.

Machine Learning 52

Machine Learning Machine Learning Cross Validation Decision Trees 52

Mlearning.ai

FEBRUARY 10, 2024

However, what drove the development of Bayes’ Theorem, and how does it differ from traditional decision-making methods such as decision trees? Traditional models, such as decision trees, often rely on a deterministic approach where decisions branch out based on known conditions. 466 accuracy 0.77

Algorithm 52

Algorithm Decision Trees Cross Validation Machine Learning 52

Heartbeat

OCTOBER 31, 2023

Before continuing, revisit the lesson on decision trees if you need help understanding what they are. We can compare the performance of the Bagging Classifier and a single Decision Tree Classifier now that we know the baseline accuracy for the test dataset. Bagging is a development of this idea.

Decision Trees 52

Decision Trees Deep Learning Deep Learning Cross Validation 52

Pickl AI

DECEMBER 10, 2024

They vary significantly between model types, such as neural networks , decision trees, and support vector machines. Decision Trees Hyperparameters such as the maximum depth of the tree and the minimum samples required to split a node control the complexity of the tree and help prevent overfitting.

Machine Learning 52

Machine Learning Machine Learning Cross Validation Decision Trees 52

Pickl AI

JULY 12, 2024

Decision Trees Decision trees recursively partition data into subsets based on the most significant attribute values. Python’s Scikit-learn provides easy-to-use interfaces for constructing decision tree classifiers and regressors, enabling intuitive model visualisation and interpretation.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Pickl AI

FEBRUARY 12, 2024

Machine Learning Algorithms Candidates should demonstrate proficiency in a variety of Machine Learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks. What is cross-validation, and why is it used in Machine Learning?

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Pickl AI

MAY 17, 2023

K-fold Cross Validation ML experts use cross-validation to resolve the issue. You train a model on the training set using a decision tree algorithm, and you achieve an accuracy of 90% on the training set and 75% on the testing set. How to Avoid Overfitting in Machine Learning?

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

NOVEMBER 18, 2024

For example, linear regression is typically used to predict continuous variables, while decision trees are great for classification and regression tasks. Decision trees are easy to interpret but prone to overfitting. predicting house prices), Linear Regression, Decision Trees, or Random Forests could be good choices.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Pickl AI

JANUARY 3, 2024

EDA, imputation, encoding, scaling, extraction, outlier handling, and cross-validation ensure robust models. Feature importance from trees Objective: Leveraging decision tree-based models to assess feature importance.

Machine Learning 52

Machine Learning Machine Learning Exploratory Data Analysis Cross Validation 52

Pickl AI

SEPTEMBER 12, 2024

Cross-Validation: A model evaluation technique that assesses how well a model will generalise to an independent dataset. Decision Trees: A supervised learning algorithm that creates a tree-like model of decisions and their possible consequences, used for both classification and regression tasks.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Iguazio

JUNE 25, 2023

Use techniques such as sequential analysis, monitoring distribution between different time windows, adding timestamps to the decision tree based classifier, and more. In some cases, cross-validation techniques like k-fold cross-validation or stratified sampling may be used to get more reliable estimates of performance.

ML 52

ML ML Clustering Cross Validation 52

Pickl AI

OCTOBER 15, 2024

Techniques like linear regression, time series analysis, and decision trees are examples of predictive models. At each node in the tree, the data is split based on the value of an input variable, and the process is repeated recursively until a decision is made.

Decision Trees 52

Decision Trees Hypothesis Testing Clustering Data Analysis 52

Mlearning.ai

MAY 23, 2023

Decision trees are more prone to overfitting. Some algorithms that have low bias are Decision Trees, SVM, etc. Hence, we have various classification algorithms in machine learning like logistic regression, support vector machine, decision trees, Naive Bayes classifier, etc. character) is underlined or not.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

Towards AI

JULY 19, 2023

(Check out the previous post to get a primer on the terms used) Outline Dealing with Class Imbalance Choosing a Machine Learning model Measures of Performance Data Preparation Stratified k-fold Cross-Validation Model Building Consolidating Results 1. among supervised models and k-nearest neighbors, DBSCAN, etc.,

Machine Learning 40

Machine Learning Machine Learning K-nearest Neighbors Support Vector Machines 40

Pickl AI

DECEMBER 12, 2024

Introduction Boosting is a powerful Machine Learning ensemble technique that combines multiple weak learners, typically decision trees, to form a strong predictive model. Lets explore the mathematical foundation, unique enhancements, and tree-pruning strategies that make XGBoost a standout algorithm. Lower values (e.g.,

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Pickl AI

NOVEMBER 28, 2024

Decision Trees These trees split data into branches based on feature values, providing clear decision rules. Unit testing ensures individual components of the model work as expected, while integration testing validates how those components function together.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Mlearning.ai

NOVEMBER 29, 2023

Decision Trees ML-based decision trees are used to classify items (products) in the database. In its core, lie gradient-boosted decision trees. For instance, when used with decision trees, it learns to outline the hardest-to-classify data instances over time. But the results should be worth it.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

PyImageSearch

JANUARY 23, 2023

The reasoning behind that is simple; whatever we have learned till now, be it adaptive boosting, decision trees, or gradient boosting, have very distinct statistical foundations which require you to get your hands dirty with the math behind them. , you already know that our approach in this series is math-heavy instead of code-heavy.

Deep Learning 85

Deep Learning Deep Learning Algorithm Decision Trees 85

Pickl AI

FEBRUARY 4, 2024

From linear regression to decision trees, Alteryx provides robust statistical models for forecasting trends and making informed decisions. Alteryx’s validation tools, such as the Cross-Validation Tool, ensure the accuracy and reliability of predictive models.

Data Preparation 52

Data Preparation Tableau Data Visualization SQL 52

Pickl AI

MAY 24, 2023

Its modified feature includes the cross-validation that allowing it to use more than one metric. LightGBM Gradient Boosting is a significant machine learning toolbox which helps developers in developing innovative algorithms by utilising defined fundamental models, specifically decision trees.

Data Science 52

Data Science Python Machine Learning Machine Learning 52

Pickl AI

AUGUST 9, 2024

Key topics include: Supervised Learning Understanding algorithms such as linear regression, decision trees, and support vector machines, and their applications in Big Data. Model Evaluation Techniques for evaluating machine learning models, including cross-validation, confusion matrix, and performance metrics.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

NOVEMBER 2, 2023

linear regression, decision trees , SVM) – Understanding about the perfect fit for using each algorithm – Parameters and hyperparameters to tune Click here to access -> Cheat sheet for Key Machine Learning Algorithms Deep Learning Concepts and Neural Network Architectures – Neural network components and their functions (e.g.,

Data Scientist 52

Data Scientist Data Science Data Visualization Machine Learning 52

Pickl AI

APRIL 26, 2024

Techniques such as cross-validation, regularisation , and feature selection can prevent overfitting. What are the advantages and disadvantages of decision trees ? Overfitting occurs when a model learns the training data too well, including noise and irrelevant patterns, leading to poor performance on unseen data.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

Mlearning.ai

APRIL 13, 2023

Gaussian kernels are commonly used for classification problems that involve non-linear boundaries, such as decision trees or neural networks. Laplacian Kernels Laplacian kernels, also known as Laplacian of Gaussian (LoG) kernels, are used in decision trees or neural networks like image processing for edge detection.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Support Vector Machines 52

Dataconomy

SEPTEMBER 1, 2023

By combining, for example, a decision tree with a support vector machine (SVM), these hybrid models leverage the interpretability of decision trees and the robustness of SVMs to yield superior predictions in medicine. The decision tree algorithm used to select features is called the C4.5

Decision Trees 195

Decision Trees Machine Learning Machine Learning Support Vector Machines 195

DagsHub

APRIL 21, 2024

It offers implementations of various machine learning algorithms, including linear and logistic regression , decision trees , random forests , support vector machines , clustering algorithms , and more. There is no licensing cost for Scikit-learn, you can create and use different ML models with Scikit-learn for free.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Mlearning.ai

JANUARY 29, 2024

Data Science Project — Build a Decision Tree Model with Healthcare Data Using Decision Trees to Categorize Adverse Drug Reactions from Mild to Severe Photo by Maksim Goncharenok Decision trees are a powerful and popular machine learning technique for classification tasks.

Decision Trees 52

Decision Trees Data Science Exploratory Data Analysis Data Analysis 52

Heartbeat

MAY 29, 2023

The weak models can be trained using techniques such as decision trees or neural networks, and the outputs are combined using techniques such as weighted averaging or gradient boosting. Use a representative and diverse validation dataset to ensure that the model is not overfitting to the training data.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78

Mlearning.ai

JULY 14, 2023

Random forests inherit the benefits of a decision tree model whilst improving upon the performance by reducing the variance. —  Jeremy Jordan Random Forest is a popular and powerful ensemble learning algorithm that combines multiple decision trees to generate accurate and stable predictions.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Cross Validation 52