Klassifier No Code Machine Learning
Why You Should Use Automated Machine Learning?
Machine Learning uses various methods and algorithms to process data and build a model in order to make accurate predictions.
It requires knowledge of machine learning algorithms and skills of computer science to manually construct a machine learning model and this reduces the usability and functionality for non-technical users.
Even for experts and those who are eligible to manually construct a machine learning model, it is very time-consuming and energy-consuming and they have to go through all the steps, in order to accomplish tasks and achieve to desired results.
This process is dependent human and it is repetitive. Machine learning experts have to spend most of their time cleaning, preprocessing and preparing data in order to build the model. Also, human errors and bias are possible and they cause model inaccuracies.
Below are the simplified steps of building a machine learning model process:
However, automated machine learning let us easily implement machine learning models and reach to solutions by automating these steps.
Automated Machine Learning is available to everyone. You don’t need to be a data scientist or machine learning expert to build your own classification model.
How automated machine learning works: you should upload the data and Automated Machine Learning then tests different Machine Learning models and algorithms, and chooses the best working Machine Learning model, and monitors its performance.
With automated machine learning, you can be sure about the accuracy. Because automated machine learning reduces the possibilities of model inaccuracies which are caused by human errors or bias and leads to better classification models and achieves a higher degree of accuracy.
Automated machine learning, makes the entire process simple and automated.
Automated machine learning use cases:
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Classification problems
In a classification problem, we want to build a classification model that predicts which labels should be assigned from a fixed number of classes or labels to data.
Common classification examples include object detection, handwriting recognition, and fraud detection.
Regression problems
Regression models predict numeric values. Very common examples include sales price prediction and house price prediction.
Feature selection
If the machine learning algorithm don’t select the features correctly, it will have a huge effect on accuracy of the model as well as the build time. But with automated machine learning, feature selection is done more efficiently.
Algorithm Selection
The process of finding the optimal algorithm and selecting the best modeling algorithm giving the highest accuracy can be very time-consuming and requires use of different evaluation methods. However, automated machine learning selects the most suitable algorithms for the machine learning problem by an automated process which saves much time and energy and gives the most accurate prediction.
Model Tuning
When tuning a model, reaching to the best hyperparameters is very critical and has huge effect on the accuracy of the model, but the hyperparameters that produce the highest accuracy and the most accurate predictions are modified through a trial-and-error process if they are set manually, which is naturally very time-consuming and energy-consuming and also requires manual evaluation and repetition of the process. However, automated machine learning makes it much easier by automatically finding the best hyperparameters for the selected algorithms of the model.
Model Evaluation
After training the machine learning model, it’s performance should be evaluated and validated, because it is important to see how the model works on new data and determine if it has accurate predictions or not and make sure that there is no overfitting nor underfitting. Only after the evaluation, if the result is successful, we can trust the accuracy of predictions by model on new data. However, automated machine learning will automatically evaluate the model’s performance and efficiency and makes the process much easier for the machine learning experts.