Mlearning.ai

FEBRUARY 2, 2023

Metrics for ML — Not looking at the model  — Explaining a ML model can help in understanding how it works and makes predictions. Techniques such as feature importance and decision trees can help you gain insights into the inner workings of a model.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

phData

AUGUST 24, 2023

With a modeled estimation of the applicant’s credit risk, lenders can make more informed decisions and reduce the occurrence of bad loans, thereby protecting their bottom line. The model learns from these labels to predict the outcome of new, unseen data. loan default or not).

ML 52

ML ML Machine Learning Machine Learning 52

Pickl AI

JUNE 4, 2024

Hyperparameter Tuning Hyperparameters are configuration settings that are external to the model and whose values cannot estimate from the data. They control the learning process of the model and can significantly impact its performance. Train the Model: Fit the model to the training data.

Cross Validation 52

Cross Validation Python Machine Learning Machine Learning 52

Mlearning.ai

NOVEMBER 29, 2023

GP has intrinsic advantages in data modeling, given its construction in the framework of Bayesian hierarchical modeling and no requirement for a priori information of function forms in Bayesian reference. Decision Trees ML-based decision trees are used to classify items (products) in the database.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

AWS Machine Learning Blog

JUNE 22, 2023

AutoGluon is easy-to-use AutoML tool that uses automatic data processing, hyperparameter tuning, and model ensemble. The best baseline was achieved with a weighted ensemble of gradient boosted decision tree models. His interest includes generative models and sequential data modeling.

AWS 113

AWS ML ML Machine Learning 113

IBM Journey to AI blog

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.

Machine Learning 70

Machine Learning Machine Learning Data Science Big Data 70

Cambridge Intelligence

FEBRUARY 20, 2024

FREE: Managing fraud The ultimate guide to fraud detection, investigation and prevention using data visualization GET YOUR FREE GUIDE The role of new & existing technology For many years, credit card companies have relied on analytics, algorithms and decision trees to power their fraud strategy.

AI 59

AI AI Machine Learning Machine Learning 59

DagsHub

OCTOBER 14, 2024

For example, an experiment name like 'ResNet50-augmented-imagenet-exp-01' provides more information about the model architecture, dataset, and experiment number. DVC Data Version Control (DVC) is an open-source version control system that is specially designed to track not just code, but also data, models, and machine learning pipelines.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

ML @ CMU

MAY 12, 2023

This is certainly not perfect as it ignores population-level modeling (e.g. denser areas have higher risks of infection), but it makes the ML problem very simple: just plug-in existing tabular data modeling approaches! Scalable, private, and federated trees for tabular data.

Data Silos 130

Data Silos Algorithm ML ML 130

Pickl AI

AUGUST 23, 2023

Ensure the data is cleaned, formatted, and free from inconsistencies, as accurate predictions heavily depend on the quality of input data. Model Selection and Evaluation Experiment with different Machine Learning algorithms for stock price prediction, such as linear regression, decision trees , random forests, and neural networks.

Data Science 52

Data Science Machine Learning Machine Learning Algorithm 52

Pickl AI

NOVEMBER 28, 2024

Decision Trees These trees split data into branches based on feature values, providing clear decision rules. Model Evaluation and Tuning After building a Machine Learning model, it is crucial to evaluate its performance to ensure it generalises well to new, unseen data.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

JULY 23, 2023

Data Modeling: Developing predictive models using machine learning algorithms like regression, decision trees, and neural networks. Data Cleansing: Ensuring data quality and removing outliers to improve model accuracy. Key Features: i.

Analytics 52

Analytics Analytics Predictive Analytics Data Analysis 52

DagsHub

APRIL 21, 2024

Scikit-learn provides a consistent API for training and using machine learning models, making it easy to experiment with different algorithms and techniques. It also provides tools for model evaluation , including cross-validation, hyperparameter tuning, and metrics such as accuracy, precision, recall, and F1-score.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Dataconomy

SEPTEMBER 1, 2023

Hybrid machine learning techniques excel in model selection by amalgamating the strengths of multiple models. 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.

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Decision Trees Machine Learning Machine Learning Support Vector Machines 195