Scikit is an open-source machine learning library, built on NumPy, SciPy, and matplotlib, that supports supervised and unsupervised learning. It is an efficient tool for predictive data analysis. Scikit consists of many built-in machine learning algorithms and models, called estimators. Each estimator can be fitted to some data using its fit method. It also provides various methods for model fitting, data preprocessing, model selection, model evaluation, and many other utilities.
AI/ML workflows are often composed of different parts. In Scikit, pre-processors and transformers follow the same API as the estimator objects. Estimators, a.k.a. hyper-parameters, have parameters that can be tuned. Scikit provides tools to automatically find the best parameter combinations using cross-validation. The transformer objects don’t have a predict method but rather a transform method that outputs a newly transformed sample matrix.
Transformers and estimators/predictors can be combined into a single unifying object called a Pipeline. A Pipeline generally consists of a pre-processing step that transforms or imputes the data and a final predictor that predicts target values. Pipeline offers the same API as a regular estimator, it can be fitted and used for prediction. Using a Pipeline this way also prevents data leakage.
What Are the Features of Scikit?
- Identifying which category an object belongs to, for example, Spam detection, and image recognition.
- Predicting a continuous-valued attribute associated with an object, such as stock prices.
- Automatic grouping of similar objects into sets, typically used for customer segmentation, and group experiment outcomes.
- Reducing the number of random variables to consider, which is efficient for visualization with increased efficiency.
- Comparing, validating, and choosing parameters and models which improve accuracy using parameter tuning.
What Are the Advantages of Working on Scikit on Cloud?
Working on Scikit projects on cloud resources can solve many problems associated with running the application on physical desktop computers.
- Resources like number CPUs, RAM/memory, and storage, are scalable as per requirements.
- It provides remote access to powerful resources for running computing-resource intensive AI/ML workloads if the project requirement is such.
- You can access your Scikit on the cloud from anywhere in the world from any desktop/laptop/mobile device with internet connectivity.
- It is possible to store large volumes of Scikit projects/databases in the cloud.
Apps4Rent Can Help
Apps4Rent offers virtual desktops hosted on the cloud which are scalable based on your requirement for exploring Scikit. Since the virtual/remote desktop is on the cloud, it can be accessed from any local PC/laptop, Windows, or Mac, which has internet connectivity. No configuration change is required on your local PC/laptop. Call, chat or email our virtual/remote desktop specialists, available 24/7 for assistance.