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Scikit-learn VS LifeSum

Compare Scikit-learn VS LifeSum and see what are their differences

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Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

LifeSum logo LifeSum

Set a weight goal and we'll tell you how to reach it!
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • LifeSum Landing page
    Landing page //
    2023-02-03

Scikit-learn

Website
scikit-learn.org
$ Details
Release Date
-
Edit details

LifeSum

Website
lifesum.com
$ Details
-
Release Date
2013 January
Startup details
Country
Sweden
State
Stockholms Lan
City
Stockholm
Founder(s)
Henrik Torstensson
Employees
10 - 19
Edit details

Scikit-learn features and specs

  • Ease of Use
    Scikit-learn provides a high-level interface for common machine learning algorithms, making it easy for beginners and professionals to implement complex models with minimal coding.
  • Extensive Documentation and Community Support
    The library has comprehensive documentation and a large, active community. This makes it easy to find tutorials, examples, and solutions to common problems.
  • Integration with Other Libraries
    Scikit-learn integrates well with other scientific computing libraries such as NumPy, SciPy, and pandas, allowing for seamless data manipulation and analysis.
  • Variety of Algorithms
    It offers a wide array of machine learning algorithms for tasks such as classification, regression, clustering, and dimensionality reduction.
  • Performance
    Designed with performance in mind, many of the algorithms are optimized and some even support multicore processing.

Possible disadvantages of Scikit-learn

  • Limited Deep Learning Support
    Scikit-learn is primarily focused on traditional machine learning algorithms and does not offer support for deep learning models, unlike libraries like TensorFlow or PyTorch.
  • Not Ideal for Large-Scale Data
    While Scikit-learn performs well for moderate-sized datasets, it may not be the best choice for extremely large datasets or big data applications.
  • Lack of Online Learning Algorithms
    The library has limited support for online learning algorithms, which are useful for scenarios where data arrives in a stream and model needs to be updated incrementally.
  • Less Flexibility in Customization
    It can be less flexible compared to lower-level libraries when highly customized or specific implementations are needed.
  • Dependency Overhead
    Scikit-learn relies on several other Python libraries like NumPy and SciPy, which might require users to manage multiple dependencies.

LifeSum features and specs

  • User-Friendly Interface
    LifeSum boasts an intuitive and visually appealing interface, making it easy for users to navigate through the app and track their dietary habits.
  • Personalized Plans
    The app offers customized meal and exercise plans based on individual health goals and dietary preferences, ensuring a tailored experience for each user.
  • Comprehensive Nutritional Information
    LifeSum provides detailed nutritional information for a vast database of foods, helping users make informed dietary choices.
  • Integration with Other Apps & Devices
    The app can sync with various other health and fitness apps, as well as wearable devices, providing a cohesive approach to health tracking.
  • Barcode Scanner
    The barcode scanning feature allows users to quickly log food items by scanning their packaging, saving time and enhancing accuracy.

Possible disadvantages of LifeSum

  • Premium Features Locked
    Many advanced features and personalized plans are only available through a paid subscription, limiting free users' experience.
  • Data Accuracy
    The app relies on user-entered data for tracking food and exercise, which can sometimes lead to inaccuracies or inconsistencies.
  • Limited Community Features
    Unlike some other health apps, LifeSum offers limited social or community features, which may be a drawback for users seeking peer support.
  • Overwhelming for Beginners
    New users might find the plethora of options and features a bit overwhelming at first, requiring some time to learn and adapt.
  • Calorie-Based Approach
    The app primarily focuses on calorie counting, which might not be suitable for users looking for alternative approaches to healthy eating.

Analysis of Scikit-learn

Overall verdict

  • Yes, Scikit-learn is generally regarded as a good library for machine learning, especially for beginners and intermediate users who need reliable tools with efficient implementation of numerous algorithms.

Why this product is good

  • Scikit-learn is considered a good machine learning library because it provides a wide range of state-of-the-art algorithms for supervised and unsupervised learning. It is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. The library is well-documented, easy to use, and has a consistent API that simplifies the integration of different algorithms. Furthermore, there's a strong community and continuous development, which means it is well-maintained and updated regularly with new features and improvements.

Recommended for

  • Beginners learning machine learning concepts and application.
  • Data scientists and engineers looking for a robust and efficient toolkit to build and deploy machine learning models.
  • Researchers who need an easy-to-use library that facilitates the experimentation of various algorithms.
  • Developers who require a seamless, Python-based machine learning library that integrates well with other data analysis tools and environments.

Scikit-learn videos

+ Add

Learning Scikit-Learn (AI Adventures)

More videos:

  • Review - Python Machine Learning Review | Learn python for machine learning. Learn Scikit-learn.

LifeSum videos

+ Add

LIFESUM Worth Your Time?? | Lifesum App Review | How to use Lifesum Effectively

More videos:

  • Review - Which is Better? Lifesum vs. MyFitnessPal
  • Review - WHAT I EAT IN A DAY! / With Lifesum

Category Popularity

0-100% (relative to Scikit-learn and LifeSum)

Scikit-learn

LifeSum

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Health And Fitness
0 0%
Health And Fitness
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Sport & Health
0 0%
Sport & Health
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Scikit-learn and LifeSum

Scikit-learn Reviews

15 data science tools to consider using in 2021
Scikit-learn is an open source machine learning library for Python that's built on the SciPy and NumPy scientific computing libraries, plus Matplotlib for plotting data. It supports both supervised and unsupervised machine learning and includes numerous algorithms and models, called estimators in scikit-learn parlance. Additionally, it provides functionality for model...
Source: searchbusinessanalytics.techtarget.com

LifeSum Reviews

Top Alternatives to MyFitnessPal
Lifesum offers a balanced approach to diet and exercise tracking with an attractive and user-friendly interface. It provides diet plans, meal suggestions, exercise logging, and health tips. The app's interface is visually appealing and easy to navigate, but it has a less accurate food database and occasional glitches, which can be frustrating for users seeking reliable...
Source: calsnaps.com
10 Best MyFitnessPal Alternatives
Lifesum, a comprehensive fitness and health application, aids in monitoring people's diet, exercise, and the state of their body. Moreover, this myfitnesspal alternative free which includes customized meal schedules, valuable tips and tricks to stay healthy, and regular workouts adapted for personal objectives. It is myfitnesspal alternative with barcode scanner.
Source: www.geeksforgeeks.org
The 8 Best Calorie Counter Apps
Lifesum is very easy to use. Its home page shows total calorie and macro intake and a breakdown of foods and calories per meal, which you can log manually or with a barcode scanner. You can also create food entries, meals, and recipes.
Source: www.healthline.com

Social recommendations and mentions

Based on our record, Scikit-learn should be more popular than LifeSum. It has been mentiond 31 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Scikit-learn mentions (31)

  • Must-Know 2025 Developer’s Roadmap and Key Programming Trends
    Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 4 months ago
  • 🚀 Launching a High-Performance DistilBERT-Based Sentiment Analysis Model for Steam Reviews 🎮🤖
    Scikit-learn (optional): Useful for additional training or evaluation tasks. - Source: dev.to / 6 months ago
  • Essential Deep Learning Checklist: Best Practices Unveiled
    How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations. - Source: dev.to / about 1 year ago
  • How to Build a Logistic Regression Model: A Spam-filter Tutorial
    Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / over 1 year ago
  • Link Prediction With node2vec in Physics Collaboration Network
    Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / about 2 years ago
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LifeSum mentions (8)

  • From Low to High (52+) VO2 max in 14 months
    A last note to my progress is that I started using Lifesum to track calorie intake and macro nutrients after my weight loss, in order to find my balance and gain a more healthy relationship with eating - I learned so much from that. I was straight up practising malnutrition and had a very unhealthy fear of carbs and fat for a long time - but I also needed to loose that weight, maybe just not THAT fast 🙈. Source: about 2 years ago
  • Tracking tools recommendations?
    I don't have the premium version but if you're willing to shell the $, Lifesum has a beautiful interface, barcode scanning, recipes, and nutrition tracking info. You'll get macros at the free level. Source: over 2 years ago
  • Fantastic Success, but Wrapping Up My Noom Experience Nonetheless. I'm Over It.
    *** For what it's worth, I'm switching to Lifesum for tracking calories. I looked at the majority of major apps, and this seems like it fits best for me. ***. Source: over 2 years ago
  • Favourite calorie/meal tracker?
    I use Lifesum. Best user experience from all the apps I’ve used before. It’s paid but I think it’s pretty cheap ($23 /year) https://lifesum.com. Source: almost 3 years ago
  • I need help putting together a meal plan. What are the best subs to get help/other resources for that?
    I’ve only tried Lifesum and Yazio. Recommend them both. Source: almost 3 years ago
View more

What are some alternatives?

When comparing Scikit-learn and LifeSum, you can also consider the following products

OpenCV - OpenCV is the world's biggest computer vision library

MyFitnessPal - Track the number of calories that you consume each day with MyFitnessPal. The app also lets you create a diet and track the exercise that you complete each day whether it's walking, running or some other type of program.

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

Cronometer - A big trend in today’s world is health and fitness, particularly in recording nutritional information. There are several options available to achieve this result.

NumPy - NumPy is the fundamental package for scientific computing with Python

Eat This Much - Eat This Much is an app that helps with meal planning for the week or the month.

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