AWS Machine Learning Blog

FEBRUARY 25, 2025

Classification algorithms like support vector machines (SVMs) are especially well-suited to use this implicit geometry of the data. To determine the best parameter values, we conducted a grid search with 10-fold cross-validation, using the F1 multi-class score as the evaluation metric.

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Pickl AI

JULY 26, 2023

Unstable Support Vector Machines (SVM) Support Vector Machines can be prone to high variance if the kernel used is too complex or if the cost parameter is not properly tuned. Regular cross-validation and model evaluation are essential to maintain this equilibrium.

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Pickl AI

JANUARY 8, 2025

RFE works effectively with algorithms like Support Vector Machines (SVMs) and linear regression. Here, we discuss two critical aspects: the impact on model accuracy and the use of cross-validation for comparison. The model is trained at each step, and features are ranked according to their contribution.

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Pickl AI

DECEMBER 10, 2024

They vary significantly between model types, such as neural networks , decision trees, and support vector machines. Combine with cross-validation to assess model performance reliably. They define the model’s capacity to learn and how it processes data.

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Mlearning.ai

APRIL 13, 2023

Before we discuss the above related to kernels in machine learning, let’s first go over a few basic concepts: Support Vector Machine , S upport Vectors and Linearly vs. Non-linearly Separable Data. The linear kernel is ideal for linear problems, such as logistic regression or support vector machines ( SVMs ).

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Pickl AI

JULY 12, 2024

Support Vector Machines (SVM) SVMs classify data points by finding the optimal hyperplane that maximises the margin between classes. Python supports diverse model validation and evaluation techniques, which are crucial for optimising model accuracy and generalisation.

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Pickl AI

NOVEMBER 28, 2024

Support Vector Machines (SVM) SVMs are powerful classifiers that separate data into distinct categories by finding an optimal hyperplane. 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.

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Heartbeat

JUNE 14, 2023

Support vector machine classifiers as applied to AVIRIS data.” Cross Validated] Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners. PMLR, 2017. [2]

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Mlearning.ai

NOVEMBER 30, 2023

Solution : Implement pruning techniques to limit the depth of the tree, and use cross-validation to ensure the model generalizes well to unseen data. Solution : Combine decision trees with other algorithms, like SVMs (Support Vector Machines), that can capture non-linear relationships.

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Pickl AI

AUGUST 9, 2024

Students should learn how to leverage Machine Learning algorithms to extract insights from large datasets. Key topics include: Supervised Learning Understanding algorithms such as linear regression, decision trees, and support vector machines, and their applications in Big Data.

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Mlearning.ai

MAY 23, 2023

Another example can be the algorithm of a support vector machine. Hence, we have various classification algorithms in machine learning like logistic regression, support vector machine, decision trees, Naive Bayes classifier, etc. What are Support Vectors in SVM (Support Vector Machine)?

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Pickl AI

NOVEMBER 18, 2024

spam detection), you might choose algorithms like Logistic Regression , Decision Trees, or Support Vector Machines. Cross-Validation: Instead of using a single train-test split, cross-validation involves dividing the data into multiple folds and training the model on each fold.

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Heartbeat

OCTOBER 23, 2023

By analyzing historical data and utilizing predictive machine learning algorithms like BERT, ARIMA, Markov Chain Analysis, Principal Component Analysis, and Support Vector Machine, they can assess the likelihood of adverse events, such as hospital readmissions, and stratify patients based on risk profiles.

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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.

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Pickl AI

OCTOBER 15, 2024

In more complex cases, you may need to explore non-linear models like decision trees, support vector machines, or time series models. Model Validation Model validation is a critical step to evaluate the model’s performance on unseen data. Model selection requires balancing simplicity and performance.

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Mlearning.ai

JANUARY 31, 2023

left: neutral pose — do nothing | right: fist  — close gripper | Photos from myo-readings-dataset left: extension  — move forward | right: flexion  — move backward | Photos from myo-readings-dataset This project uses the scikit-learn implementation of a Support Vector Machine (SVM) trained for gesture recognition.

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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.

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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. For example, you might want to build a ML model that determines if an email is spam or not.

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DrivenData Labs

MAY 1, 2025

Some participants combined a transformer neural network with a tree-based model or support vector machine (SVM). All of the top models used the encoder of a pretrained model to generate embeddings, and added their own classification layer. The most common model was Whisper , followed by Wav2Vec and HuBERT.

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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.,

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Dataconomy

SEPTEMBER 1, 2023

Hybrid machine learning techniques integrate clinical, genetic, lifestyle, and omics data to provide a comprehensive view of patient health ( Image credit ) The choice of an appropriate model is critical in predictive modeling. Hybrid machine learning techniques excel in model selection by amalgamating the strengths of multiple models.

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