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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Mlearning.ai

MAY 23, 2023

Hey guys, in this blog we will see some of the most asked Data Science Interview Questions by interviewers in [year]. Data science has become an integral part of many industries, and as a result, the demand for skilled data scientists is soaring. What is Data Science?

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NYU Center for Data Science

AUGUST 2, 2023

He received his PhD in Electrical Engineering from Stanford University, completing a dissertation on the “ Approximate message passing algorithms for compressed sensing.” Prior to his work at Columbia, Arian was a postdoctoral scholar at Rice University. He has taught various calculus and statistics courses from PhD to BSc levels.

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DrivenData Labs

MAY 24, 2023

Currently pursuing graduate studies at NYU's center for data science. Alejandro Sáez: Data Scientist with consulting experience in the banking and energy industries currently pursuing graduate studies at NYU's center for data science. What motivated you to compete in this challenge? The federated learning aspect.

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Pickl AI

MARCH 26, 2024

Summary: The KNN algorithm in machine learning presents advantages, like simplicity and versatility, and challenges, including computational burden and interpretability issues. Nevertheless, its applications across classification, regression, and anomaly detection tasks highlight its importance in modern data analytics methodologies.

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Pickl AI

MAY 24, 2023

To help you understand Python Libraries better, the blog will explain a Python Libraries for Data Science List which you can learn about. This may include for instance in Machine Learning, Data Science, Data Visualisation, image and Data Manipulation. What is a Python Library?

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AWS Machine Learning Blog

FEBRUARY 25, 2025

For the classfier, we employed a classic ML algorithm, k-NN, using the scikit-learn Python module. The following figure illustrates the F1 scores for each class plotted against the number of neighbors (k) used in the k-NN algorithm. The SVM algorithm requires the tuning of several parameters to achieve optimal performance.

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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.

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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. Consider a scenario where a data science team without dedicated MLOps practices is developing an ML model for sales forecasting.

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Mlearning.ai

FEBRUARY 15, 2024

Data Science Project — Predictive Modeling on Biological Data Part III — A step-by-step guide on how to design a ML modeling pipeline with scikit-learn Functions. Photo by Unsplash Earlier we saw how to collect the data and how to perform exploratory data analysis. You can refer part-I and part-II of this article.

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Dataconomy

JUNE 29, 2023

Python machine learning packages have emerged as the go-to choice for implementing and working with machine learning algorithms. These libraries, with their rich functionalities and comprehensive toolsets, have become the backbone of data science and machine learning practices. Why do you need Python machine learning packages?

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Heartbeat

FEBRUARY 20, 2023

First-time project and model registration Photo by Isaac Smith on Unsplash The world of machine learning and data science is awash with technicalities. This could involve tuning hyperparameters and combining different algorithms in order to leverage their strengths and come up with a better-performing model.

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DrivenData Labs

MARCH 28, 2023

Team Just4Fun ¶ Qixun Qu Hongwei Fan Place: 2nd Place Prize: $2,000 Hometown: Chengdu, Sichuan, China (Qixun Qu) and Nanjing Jiangsu, China (Hongwei Fan) Username: qqggg , HongweiFan Background: I (qqggg, Qixun Qu in real name) am a vision algorithm developer and focus on image and signal analysis.

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DrivenData Labs

JANUARY 11, 2023

The results of this GCMS challenge could not only support NASA scientists to more quickly analyze data, but is also a proof-of-concept of the use of data science and machine learning techniques on complex GCMS data for future missions. I teach computer programming, data science and software engineering courses.

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Mlearning.ai

SEPTEMBER 24, 2023

A brute-force search is a general problem-solving technique and algorithm paradigm. Figure 1: Brute Force Search It is a cross-validation technique. It trains several models using k — 1 of the folds as training data. The remaining fold is used as test data to compute a performance measure. Reference: Chopra, R.,

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DrivenData Labs

MAY 22, 2024

Other data sources were experimented with, and teams expressed that they would continue to experiment with data sources in the following competition stages. Gradient-boosted trees were popular modeling algorithms among the teams that submitted model reports, including the first- and third-place winners.

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Pickl AI

NOVEMBER 2, 2023

It serves as a handy quick-reference tool to assist data professionals in their work, aiding in data interpretation, modeling , and decision-making processes. In the fast-paced world of Data Science, having quick and easy access to essential information is invaluable when using a repository of Cheat sheets for Data Scientists.

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AWS Machine Learning Blog

JANUARY 26, 2023

Data scientists train multiple ML algorithms to examine millions of consumer data records, identify anomalies, and evaluate if a person is eligible for credit. The Best Egg data science team uses Amazon SageMaker Studio for building and running Jupyter notebooks.

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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.

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Pickl AI

MAY 17, 2023

Machine learning empowers the machine to perform the task autonomously and evolve based on the available data. However, while working on a Machine Learning algorithm , one may come across the problem of underfitting or overfitting. K-fold Cross Validation ML experts use cross-validation to resolve the issue.

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Pickl AI

FEBRUARY 22, 2024

Summary: Dive into programs at Duke University, MIT, and more, covering Data Analysis, Statistical quality control, and integrating Statistics with Data Science for diverse career paths. offer modules in Statistical modelling, biostatistics, and comprehensive Data Science bootcamps, ensuring practical skills and job placement.

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Heartbeat

OCTOBER 23, 2023

Revolutionizing Healthcare through Data Science and Machine Learning Image by Cai Fang on Unsplash Introduction In the digital transformation era, healthcare is experiencing a paradigm shift driven by integrating data science, machine learning, and information technology.

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DrivenData Labs

APRIL 10, 2024

In the Kelp Wanted challenge, participants were called upon to develop algorithms to help map and monitor kelp forests. Winning algorithms will not only advance scientific understanding, but also equip kelp forest managers and policymakers with vital tools to safeguard these vulnerable and vital ecosystems.

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AWS Machine Learning Blog

DECEMBER 13, 2024

Were using Bayesian optimization for hyperparameter tuning and cross-validation to reduce overfitting. The data set contains features like opportunity name, opportunity details, needs, associated product name, product details, product groups. This helps make sure that the clustering is accurate and relevant.

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Pickl AI

DECEMBER 12, 2024

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

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Mlearning.ai

FEBRUARY 3, 2023

Parameters are updated by the learning algorithm during training, based on the training data and optimization algorithm, while hyperparameters are set by the practitioner and are not learned from the data. B) Cross-Validation (CV): CV in “GridSearchCV” stands for Cross-Validation.

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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")

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Mlearning.ai

NOVEMBER 30, 2023

The accuracy of these predictions typically surpasses that of a single decision tree, showcasing the strength of random forests in handling complex data sets in data science. This improvement often results in high accuracy, making GBMs a powerful tool in data science for solving complex problems.

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Pickl AI

AUGUST 29, 2023

Parameter Estimation: Determine the parameters if the model by finding relevance to the data. This may involve finding values that best represent to observed data. Model Evaluation: Assess the quality of the midel by using different evaluation metrics, cross validation and techniques that prevent overfitting.

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Pickl AI

FEBRUARY 19, 2025

Algorithms like AdaBoost, XGBoost, and LightGBM power real-world finance, healthcare, and NLP applications. Despite computational costs, Boosting remains vital for handling complex data and optimising AI models for high-performance decision-making. This blog explores how Boosting works and its popular algorithms.

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Pickl AI

SEPTEMBER 18, 2024

Summary: Machine Learning Engineer design algorithms and models to enable systems to learn from data. A Machine Learning Engineer plays a crucial role in this landscape, designing and implementing algorithms that drive innovation and efficiency. In finance, they build models for risk assessment or algorithmic trading.

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Mlearning.ai

NOVEMBER 23, 2023

This simplifies the process of model selection and evaluation, making it easier than ever to choose the right algorithm for your supervised learning task. random_state: You can set a random seed for reproducibility if the algorithms used by Lazypredict have any random components.

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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.

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Mlearning.ai

FEBRUARY 10, 2023

For example, if you are using regularization such as L2 regularization or dropout with your deep learning model that performs well on your hold-out-cross-validation set, then increasing the model size won’t hurt performance, it will stay the same or improve. The only drawback of using a bigger model is computational cost.

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Ocean Protocol

AUGUST 8, 2023

We are excited to announce the winners of the first-ever invite-only data challenge hosted by Ocean Protocol! We received great feedback when tasked our data science community with the original sentiment analysis of the OCEAN token challenge, and now are able to share results from the second leg of this frontier.

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Mlearning.ai

JUNE 27, 2023

Originally used in Data Mining, clustering can also serve as a crucial preprocessing step in various Machine Learning algorithms. By applying clustering algorithms, distinct clusters or groups can be automatically identified within a dataset. The optimal value for K can be found using ideas like Cross Validation (CV).

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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. Conversely, a smaller batch size can lead to slower convergence but can be more memory-efficient and may generalize better to new data.

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