DrivenData Labs

JANUARY 22, 2025

Final Stage Overall Prizes where models were rigorously evaluated with cross-validation and model reports were judged by a panel of experts. The cross-validations for all winners were reproduced by the DrivenData team. Lower is better. Unsurprisingly, the 0.10 quantile was easier to predict than the 0.90

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Cross Validation Machine Learning Machine Learning ML 147

DataRobot

DECEMBER 20, 2021

Multimodal Clustering. Multimodal Clustering provides users with a one-click, one line-of-code experience to build and deploy clustering models on any data, including images. Select “Start” and let DataRobot AI Cloud Platform do the work for you.

Clustering 97

Clustering Deep Learning Deep Learning Exploratory Data Analysis 97

Mlearning.ai

APRIL 10, 2023

No Problem: Using DBSCAN for Outlier Detection and Data Cleaning Photo by Mel Poole on Unsplash DBSCAN stands for Density-Based Spatial Clustering of Applications with Noise. Our goal is to cluster these points into groups that are densely packed together. We stop when we cannot assign more core points to the first cluster.

Algorithm 52

Algorithm Clustering Cross Validation Machine Learning 52

AWS Machine Learning Blog

DECEMBER 13, 2024

The approach uses three sequential BERTopic models to generate the final clustering in a hierarchical method. Clustering We use the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) method to form different use case clusters. Lastly, a third layer is used for some of the clusters to create sub-topics.

ML 90

ML ML Clustering AWS 90

Towards AI

JULY 19, 2023

Use the following methods- Validate/compare the predictions of your model against actual data Compare the results of your model with a simple moving average Use k-fold cross-validation to test the generalized accuracy of your model Use rolling windows to test how well the model performs on the data that is one step or several steps ahead of the current (..)

Cross Validation 98

Cross Validation Clustering EDA Data Preparation 98

Iguazio

JUNE 25, 2023

Clustering Metrics Clustering is an unsupervised learning technique where data points are grouped into clusters based on their similarities or proximity. Evaluation metrics include: Silhouette Coefficient - Measures the compactness and separation of clusters.

ML 52

ML ML Clustering Cross Validation 52

AWS Machine Learning Blog

JANUARY 26, 2023

To reduce variance, Best Egg uses k-fold cross validation as part of their custom container to evaluate the trained model. After the first training job is complete, the instances used for training are retained in the warm pool cluster. The trained model artifact is registered and versioned in the SageMaker model registry.

ML 97

ML ML Data Scientist AWS 97

Dataconomy

JUNE 29, 2023

These packages are built to handle various aspects of machine learning, including tasks such as classification, regression, clustering, dimensionality reduction, and more. These packages cover a wide array of areas including classification, regression, clustering, dimensionality reduction, and more.

Machine Learning 113

Machine Learning Machine Learning Deep Learning Deep Learning 113

Pickl AI

AUGUST 29, 2023

This could be linear regression, logistic regression, clustering , time series analysis , etc. Model Evaluation: Assess the quality of the midel by using different evaluation metrics, cross validation and techniques that prevent overfitting. This may involve finding values that best represent to observed data.

Data Scientist 52

Data Scientist Clustering Data Analysis Data Analysis 52

Mlearning.ai

JUNE 27, 2023

A Complete Guide about K-Means, K-Means ++, K-Medoids & PAM’s in K-Means Clustering. A Complete Guide about K-Means, K-Means ++, K-Medoids & PAM’s in K-Means Clustering. To address such tasks and uncover behavioral patterns, we turn to a powerful technique in Machine Learning called Clustering. K = 3 ; 3 Clusters.

Clustering 52

Clustering Algorithm Data Analyst Machine Learning 52

Pickl AI

FEBRUARY 17, 2025

Techniques such as cross-validation help assess how well a model generalises to unseen data, while optimisation algorithms fine-tune model parameters to enhance predictive capabilities Types of Machine Learning Approaches Machine Learning encompasses various approaches to enable systems to learn from data. predicting house prices).

Machine Learning 52

Machine Learning Machine Learning Supervised Learning ML 52

AWS Machine Learning Blog

FEBRUARY 25, 2025

SVM-based classifier: Amazon Titan Embeddings In this scenario, it is likely that user interactions belonging to the three main categories ( Conversation , Services , and Document_Translation ) form distinct clusters or groups within the embedding space. This doesnt imply that clusters coudnt be highly separable in higher dimensions.

Algorithm 75

Algorithm Machine Learning Machine Learning K-nearest Neighbors 75

Pickl AI

JULY 12, 2024

Python facilitates the application of various unsupervised algorithms for clustering and dimensionality reduction. K-Means Clustering K-means partition data points into K clusters based on similarities in feature space.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Pickl AI

FEBRUARY 12, 2024

Clustering algorithms such as K-means and hierarchical clustering are examples of unsupervised learning techniques. What is cross-validation, and why is it used in Machine Learning? Cross-validation is a technique used to assess the performance and generalization ability of Machine Learning models.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

AWS Machine Learning Blog

MAY 31, 2024

Following Nguyen et al , we train on chromosomes 2, 4, 6, 8, X, and 14–19; cross-validate on chromosomes 1, 3, 12, and 13; and test on chromosomes 5, 7, and 9–11. The computational resources included a cluster configured with one ml.g5.12xlarge instance, which houses four Nvidia A10G GPUs.

AWS 112

AWS ML ML Machine Learning 112

Pickl AI

NOVEMBER 28, 2024

Key techniques in unsupervised learning include: Clustering (K-means) K-means is a clustering algorithm that groups data points into clusters based on their similarities. 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

Pickl AI

OCTOBER 15, 2024

Applications : Stock price prediction and financial forecasting Analysing sales trends over time Demand forecasting in supply chain management Clustering Models Clustering is an unsupervised learning technique used to group similar data points together. Popular clustering algorithms include k-means and hierarchical clustering.

Decision Trees 52

Decision Trees Hypothesis Testing Clustering Data Analysis 52

Data Science Dojo

AUGUST 24, 2023

MLOps practices include cross-validation, training pipeline management, and continuous integration to automatically test and validate model updates. Examples include: Cross-validation techniques for better model evaluation. Managing training pipelines and workflows for a more efficient and streamlined process.

Machine Learning 195

Machine Learning Machine Learning ML ML 195

Pickl AI

NOVEMBER 18, 2024

Clustering and dimensionality reduction are common tasks in unSupervised Learning. For example, clustering algorithms can group customers by purchasing behaviour, even if the group labels are not predefined. customer segmentation), clustering algorithms like K-means or hierarchical clustering might be appropriate.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Mlearning.ai

JANUARY 31, 2023

It turned out that a better solution was to annotate data by using a clustering algorithm, in particular, I chose the popular K-means. So I simply run the K-means on the whole dataset, partitioning it into 4 different clusters. The label of a cluster was set as a label for every one of its samples. We are in the nearby of 0.9

Clustering 52

Clustering Algorithm Machine Learning Machine Learning 52

AWS Machine Learning Blog

FEBRUARY 10, 2023

Quantitative evaluation We utilize 2018–2020 season data for model training and validation, and 2021 season data for model evaluation. We perform a five-fold cross-validation to select the best model during training, and perform hyperparameter optimization to select the best settings on multiple model architecture and training parameters.

ML 88

ML ML Machine Learning Machine Learning 88

Pickl AI

SEPTEMBER 12, 2024

Clustering: An unsupervised Machine Learning technique that groups similar data points based on their inherent similarities. Cross-Validation: A model evaluation technique that assesses how well a model will generalise to an independent dataset.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Pickl AI

AUGUST 9, 2024

Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers. 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

DataRobot Blog

DECEMBER 20, 2022

For example, the model produced a RMSLE (Root Mean Squared Logarithmic Error) Cross Validation of 0.0825 and a MAPE (Mean Absolute Percentage Error) Cross Validation of 6.215. This would entail a roughly +/-€24,520 price difference on average, compared to the true price, using MAE (Mean Absolute Error) Cross Validation.

AI 59

AI AI Cross Validation Machine Learning 59

Mlearning.ai

JUNE 28, 2023

This extensive repertoire includes classification, regression, clustering, natural language processing, and anomaly detection. The compare_models() function trains all available models in the PyCaret library and evaluates their performance using cross-validation, providing a simple way to select the best-performing model.

Machine Learning 52

Machine Learning Machine Learning Data Preparation Data Science 52

Pickl AI

SEPTEMBER 17, 2024

Clustering algorithms, such as K-Means and DBSCAN, are common examples of unsupervised learning techniques. The goal in Machine Learning is to find a balance between bias and variance by choosing an appropriate model complexity and using techniques such as regularisation and cross-validation.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Machine Learning Machine Learning 52

Mlearning.ai

MAY 23, 2023

There are majorly two categories of sampling techniques based on the usage of statistics, they are: Probability Sampling techniques: Clustered sampling, Simple random sampling, and Stratified sampling. What is Cross-Validation? Cross-Validation is a Statistical technique used for improving a model’s performance.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

Pickl AI

SEPTEMBER 18, 2024

Algorithm and Model Development Understanding various Machine Learning algorithms—such as regression , classification , clustering , and neural networks —is fundamental. You should be comfortable with cross-validation, hyperparameter tuning, and model evaluation metrics (e.g., accuracy, precision, recall, F1-score).

Machine Learning 52

Machine Learning Machine Learning Algorithm Natural Language Processing 52

Pickl AI

APRIL 26, 2024

Techniques such as cross-validation, regularisation , and feature selection can prevent overfitting. Then, I would use clustering techniques such as k-means or hierarchical clustering to group customers based on similarities in their purchasing behaviour. In my previous role, we had a project with a tight deadline.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

Pickl AI

DECEMBER 10, 2024

Projecting data into two or three dimensions reveals hidden structures and clusters, particularly in large, unstructured datasets. Cross-validation ensures these evaluations generalise across different subsets of the data. This method is invaluable for eliminating noise and capturing the essence of high-dimensional datasets.

Machine Learning 52

Machine Learning Machine Learning Algorithm Deep Learning 52

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

DrivenData Labs

MAY 1, 2025

The final sub-models use broad semantic clustering, an ensemble of the provided acoustic features, a Whisper classification fine-tune, and a contrastive Whisper fine-tune, designed to focus the model on identifying features independent of age, gender, and semantic group. Cluster 0 was in English and included many people talking to an Alexa.

Decision Trees 130

Decision Trees Clustering Algorithm Machine Learning 130

The MLOps Blog

JUNE 6, 2023

Perform cross-validation using StratifiedKFold. We perform cross-validation using the StratifiedKFold method, which splits the training data into K folds, maintaining the proportion of classes in each fold. The model is trained K times, using K-1 folds for training and one fold for validation.

ML 52

ML ML Cross Validation Machine Learning 52

Dataconomy

MARCH 27, 2025

These include: Clustering techniques: Methods like KMeans organize unlabeled data into meaningful clusters. Cross-validation procedures: Essential for assessing model performance on unseen datasets. Datasets utilities: Tools for generating datasets that allow users to test model behavior.

Machine Learning 91

Machine Learning Machine Learning Cross Validation Clustering 91