Smart Data Collective

SEPTEMBER 16, 2020

Data Sourcing. Fundamental to any aspect of data science, it’s difficult to develop accurate predictions or craft a decision tree if you’re garnering insights from inadequate data sources. Objectives and Usage.

Predictive Analytics 139

Predictive Analytics Analytics Analytics Decision Trees 139

ODSC - Open Data Science

OCTOBER 7, 2024

In today’s landscape, AI is becoming a major focus in developing and deploying machine learning models. It isn’t just about writing code or creating algorithms — it requires robust pipelines that handle data, model training, deployment, and maintenance. Model Training: Running computations to learn from the data.

Machine Learning 52

Machine Learning Machine Learning AI AI 52

AWS Machine Learning Blog

JUNE 22, 2023

New machines are added continuously to the system, so we had to make sure our model can handle prediction on new machines that have never been seen in training. Data preprocessing and feature engineering In this section, we discuss our methods for data preparation and feature engineering.

AWS 101

AWS ML ML Machine Learning 101

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. Data Preparation The first step in the process is data collection and preparation. loan default or not).

ML 52

ML ML 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

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

DagsHub

APRIL 21, 2024

You need to make that model available to the end users, monitor it, and retrain it for better performance if needed. Scikit-learn provides a consistent API for training and using machine learning models, making it easy to experiment with different algorithms and techniques.

Machine Learning 59

Machine Learning Machine Learning ML ML 59