DrivenData Labs

MAY 22, 2024

Meet the Winners ¶ Prize Name 1st place Rasyid Ridha (rasyidstat) 2nd place Roman Chernenko and Vitaly Bondar (Team ck-ua) 3rd place Matthew Aeschbacher (oshbocker) Rasyid Ridha ¶ Place: 1st Prize: $25,000 Home country: Indonesia Username: rasyidstat Background: Experienced Data Scientist specializing in time series and forecasting.

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Mlearning.ai

NOVEMBER 30, 2023

Mastering Tree-Based Models in Machine Learning: A Practical Guide to Decision Trees, Random Forests, and GBMs Image created by the author on Canva Ever wondered how machines make complex decisions? Just like a tree branches out, tree-based models in machine learning do something similar. So buckle up!

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Mlearning.ai

APRIL 13, 2023

Photo by Robo Wunderkind on Unsplash In general , a data scientist should have a basic understanding of the following concepts related to kernels in machine learning: 1. Gaussian kernels are commonly used for classification problems that involve non-linear boundaries, such as decision trees or neural networks.

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Heartbeat

OCTOBER 31, 2023

Before continuing, revisit the lesson on decision trees if you need help understanding what they are. We can compare the performance of the Bagging Classifier and a single Decision Tree Classifier now that we know the baseline accuracy for the test dataset. Bagging is a development of this idea.

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Pickl AI

JULY 26, 2023

Understanding these concepts is paramount for any data scientist, machine learning engineer, or researcher striving to build robust and accurate models. As a result, the model becomes too specific to the training data and fails to generalize well to new, unseen data, leading to overfitting.

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Pickl AI

JANUARY 8, 2025

Tree-Based Methods Decision trees and ensemble methods like Random Forest and Gradient Boosting inherently perform feature selection. For tree-based models, importance scores are derived from decision splits. Lasso is particularly useful for datasets with high dimensionality.

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Pickl AI

JULY 12, 2024

Decision Trees Decision trees recursively partition data into subsets based on the most significant attribute values. Python’s Scikit-learn provides easy-to-use interfaces for constructing decision tree classifiers and regressors, enabling intuitive model visualisation and interpretation.

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Pickl AI

JANUARY 3, 2024

EDA, imputation, encoding, scaling, extraction, outlier handling, and cross-validation ensure robust models. Feature Engineering enhances model performance, and interpretability, mitigates overfitting, accelerates training, improves data quality, and aids deployment. Steps of Feature Engineering 1.

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Pickl AI

FEBRUARY 12, 2024

Data Science interviews are pivotal moments in the career trajectory of any aspiring data scientist. Having the knowledge about the data science interview questions will help you crack the interview. What is cross-validation, and why is it used in Machine Learning?

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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. Overfitting: The model performs well only for the sample training data.

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Pickl AI

SEPTEMBER 12, 2024

Data Science is the art and science of extracting valuable information from data. It encompasses data collection, cleaning, analysis, and interpretation to uncover patterns, trends, and insights that can drive decision-making and innovation.

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Pickl AI

NOVEMBER 18, 2024

For example, linear regression is typically used to predict continuous variables, while decision trees are great for classification and regression tasks. For instance, linear regression is simple and interpretable but may not capture complex relationships in the data. Different algorithms are suited to different tasks.

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Iguazio

JUNE 25, 2023

Detect Drift: Concept Drift and Data Drift Monitor for all types of drift to ensure that the ML model remains accurate and reliable. Use techniques such as sequential analysis, monitoring distribution between different time windows, adding timestamps to the decision tree based classifier, and more.

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Pickl AI

DECEMBER 12, 2024

Introduction Boosting is a powerful Machine Learning ensemble technique that combines multiple weak learners, typically decision trees, to form a strong predictive model. Lets explore the mathematical foundation, unique enhancements, and tree-pruning strategies that make XGBoost a standout algorithm. Lower values (e.g.,

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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.

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Pickl AI

AUGUST 9, 2024

Key topics include: Supervised Learning Understanding algorithms such as linear regression, decision trees, and support vector machines, and their applications in Big Data. Model Evaluation Techniques for evaluating machine learning models, including cross-validation, confusion matrix, and performance metrics.

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DagsHub

APRIL 21, 2024

Although MLOps is an abbreviation for ML and operations, don’t let it confuse you as it can allow collaborations among data scientists, DevOps engineers, and IT teams. Model Training Frameworks This stage involves the process of creating and optimizing the predictive models with labeled and unlabeled data.

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Pickl AI

APRIL 26, 2024

Overfitting occurs when a model learns the training data too well, including noise and irrelevant patterns, leading to poor performance on unseen data. Techniques such as cross-validation, regularisation , and feature selection can prevent overfitting. What are the advantages and disadvantages of decision trees ?

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Heartbeat

MAY 29, 2023

The weak models can be trained using techniques such as decision trees or neural networks, and the outputs are combined using techniques such as weighted averaging or gradient boosting. Use a representative and diverse validation dataset to ensure that the model is not overfitting to the training data.

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Dataconomy

SEPTEMBER 1, 2023

By combining, for example, a decision tree with a support vector machine (SVM), these hybrid models leverage the interpretability of decision trees and the robustness of SVMs to yield superior predictions in medicine. The decision tree algorithm used to select features is called the C4.5

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The MLOps Blog

JUNE 6, 2023

This is an ensemble learning method that builds multiple decision trees and combines their predictions to improve accuracy and reduce overfitting. The pipeline automates the entire process of preprocessing the data and training the model, making the workflow more efficient and easier to maintain. Create the ML model.

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