Smart Data Collective

JULY 1, 2023

The resulting structured data is then used to train a machine learning algorithm. Improving annotation quality is crucial for various tasks, including data labeling for machine learning models, document categorization, sentiment analysis, and more. Conduct training sessions or provide a document explaining the guidelines thoroughly.

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Cross Validation AI AI Machine Learning 93

Ocean Protocol

NOVEMBER 28, 2024

Using innovative approaches and advanced algorithms, participants modeled scenarios accounting for starting grid positions, driver performance, and unpredictable race conditions like weather changes or mid-race interruptions. Firepig refined predictions using detailed feature engineering and cross-validation.

Cross Validation 85

Cross Validation Decision Trees Data Scientist Data Science 85

Ocean Protocol

FEBRUARY 2, 2023

Indeed, the most robust predictive trading algorithms use machine learning (ML) techniques. On the optimistic side, algorithmically trading assets with predictive ML models can yield enormous gains à la Renaissance Technologies… Yet algorithmic trading gone awry can yield enormous losses as in the latest FTX scandal. Easy peasy.

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Mlearning.ai

FEBRUARY 10, 2024

Unlocking Predictive Power: How Bayes’ Theorem Fuels Naive Bayes Algorithm to Solve Real-World Problems [link] Introduction In the constantly shifting realm of machine learning, we can see that many intricate algorithms are rooted in the fundamental principles of statistics and probability. Take the Naive Bayes algorithm, for example.

Algorithm 52

Algorithm Decision Trees Cross Validation Machine Learning 52

DrivenData Labs

MAY 22, 2024

Gradient-boosted trees were popular modeling algorithms among the teams that submitted model reports, including the first- and third-place winners. Final Prize Stage : Refined models are being evaluated once again on historical data but using a more robust cross-validation procedure.

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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. We chose to compete in this challenge primarily to gain experience in the implementation of machine learning algorithms for data science.

Machine Learning 225

Machine Learning Machine Learning Data Science Decision Trees 225

Pickl AI

NOVEMBER 28, 2024

Summary: The blog discusses essential skills for Machine Learning Engineer, emphasising the importance of programming, mathematics, and algorithm knowledge. Understanding Machine Learning algorithms and effective data handling are also critical for success in the field. Below, we explore some of the most widely used algorithms in ML.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

DECEMBER 10, 2024

Introduction Hyperparameters in Machine Learning play a crucial role in shaping the behaviour of algorithms and directly influence model performance. Understanding these model-specific hyperparameters helps practitioners focus on the most important settings for a given algorithm.

Machine Learning 52

Machine Learning Machine Learning Cross Validation Decision Trees 52

Pickl AI

JANUARY 3, 2024

Feature engineering in machine learning is a pivotal process that transforms raw data into a format comprehensible to algorithms. EDA, imputation, encoding, scaling, extraction, outlier handling, and cross-validation ensure robust models. What is Feature Engineering?

Machine Learning 52

Machine Learning Machine Learning Exploratory Data Analysis Cross Validation 52

AWS Machine Learning Blog

DECEMBER 13, 2024

In both LSA and LDA, each document is treated as a collection of words only and the order of the words or grammatical role does not matter, which may cause some information loss in determining the topic. Were using Bayesian optimization for hyperparameter tuning and cross-validation to reduce overfitting.

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Pickl AI

JULY 12, 2024

Jupyter notebooks allow you to create and share live code, equations, visualisations, and narrative text documents. Their interactive nature makes them suitable for experimenting with AI algorithms and analysing data. AI algorithms may produce inaccurate or biased results without clean, relevant, and representative data.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Data Science Dojo

AUGUST 24, 2023

MLOps emphasizes the need for continuous integration and continuous deployment (CI/CD) in the ML workflow, ensuring that models are updated in real-time to reflect changes in data or ML algorithms. Examples include: Cross-validation techniques for better model evaluation.

Machine Learning 195

Machine Learning Machine Learning ML ML 195

Pickl AI

DECEMBER 12, 2024

Summary: XGBoost is a highly efficient and scalable Machine Learning algorithm. Key Features of XGBoost XGBoost (eXtreme Gradient Boosting) has earned its reputation as a powerful and efficient Machine Learning algorithm. It combines gradient boosting with features like regularisation, parallel processing, and missing data handling.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Heartbeat

MARCH 27, 2023

So I will pick the MLPClassifier algorithm for the next model. So we will write our code as follows: #our new better performing algorithm model1 = MLPClassifier(max_iter=1000, random_state = 0) #fitting model model1.fit(X, Have you tried Comet? fit(X, y) #exporting model to desired location dump(model1, "model1.joblib")

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Heartbeat

DECEMBER 19, 2023

Selection of Recommender System Algorithms: When selecting recommender system algorithms for comparative study, it's crucial to incorporate various methods encompassing different recommendation approaches. This diversity ensures a comprehensive understanding of each algorithm's performance under various scenarios.

Deep Learning 52

Deep Learning Deep Learning Algorithm Machine Learning 52

Pickl AI

DECEMBER 16, 2024

Summary: AI in Time Series Forecasting revolutionizes predictive analytics by leveraging advanced algorithms to identify patterns and trends in temporal data. Advanced algorithms recognize patterns in temporal data effectively. Key Takeaways AI automates complex forecasting processes for improved efficiency. accuracy, precision).

AI 52

AI AI Machine Learning Machine Learning 52

Pickl AI

SEPTEMBER 12, 2024

A Algorithm: A set of rules or instructions for solving a problem or performing a task, often used in data processing and analysis. 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

DECEMBER 10, 2024

By extracting key features, you allow the Machine Learning algorithm to focus on the most critical aspects of the data, leading to better generalisation. Numerical Features (Continuous vs. Discrete) Numerical features represent data quantitatively, making them the most straightforward for Machine Learning algorithms to process.

Machine Learning 52

Machine Learning Machine Learning Algorithm Deep Learning 52

Pickl AI

NOVEMBER 2, 2023

These reference guides condense complex concepts, algorithms, and commands into easy-to-understand formats. Expertise in mathematics and statistical fields is essential for deciding algorithms, drawing conclusions, and making predictions. Let’s delve into the world of cheat sheets and understand their importance.

Data Scientist 52

Data Scientist Data Science Data Visualization Machine Learning 52

Mlearning.ai

APRIL 13, 2023

Support Vector Machine Support Vector Machine ( SVM ) is a supervised learning algorithm used for classification and regression analysis. Machine learning algorithms rely on mathematical functions called “kernels” to make predictions based on input data. This is often done using techniques such as cross-validation or grid search.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Support Vector Machines 52

Pickl AI

SEPTEMBER 10, 2023

You can use techniques like grid search, cross-validation, or optimization algorithms to find the best parameter values that minimize the forecast error. Document Your Configuration: Keep a record of the selected smoothing parameters and any adjustments made over time.

Data Analyst 52

Data Analyst Cross Validation Python Data Preparation 52

DagsHub

APRIL 21, 2024

Autonomous Vehicles: Automotive companies are using ML models for autonomous driving systems including object detection, path planning, and decision-making algorithms. MLOps ensures the reliability and safety of these models through rigorous testing, validation, and continuous monitoring in real-world driving conditions.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Heartbeat

MAY 29, 2023

BERT model architecture; image from TDS Hyperparameter tuning Hyperparameter tuning is the process of selecting the optimal hyperparameters for a machine learning algorithm. 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

Pickl AI

OCTOBER 15, 2024

Popular clustering algorithms include k-means and hierarchical clustering. Model Validation Model validation is a critical step to evaluate the model’s performance on unseen data. This helps ensure that the model performs well across different data samples and is not overfitted to the training data.

Decision Trees 52

Decision Trees Hypothesis Testing Clustering Data Analysis 52

The MLOps Blog

DECEMBER 7, 2022

Given that the whole theory of machine learning assumes today will behave at least somewhat like yesterday, what can algorithms and models do for you in such a chaotic context ? This is a relatively straightforward process that handles training with cross-validation, optimization, and, later on, full dataset training.

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The MLOps Blog

JUNE 6, 2023

Experimentation: With a structured pipeline, it’s easier to track experiments and compare different models or algorithms. The preprocessing stage involves cleaning, transforming, and encoding the data, making it suitable for machine learning algorithms. Perform cross-validation using StratifiedKFold.

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Dataconomy

MARCH 27, 2025

Its user-friendly nature and extensive documentation make it accessible to newcomers while still holding great promise for seasoned practitioners. Key aspects include a focus on usability, code quality, and comprehensive documentation, ensuring that users can apply the library effectively.

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Machine Learning Machine Learning Cross Validation Clustering 91