# How to use RANSAC algorithm for Robust Regression in scikit-learn Python

by kanika Updated: May 9, 2023

Solution Kit

RANSAC is an iterative algorithm. It helps estimate the mathematical model parameters from a set of observed data. It contains outliers. It chooses a subset of the data points and then applies a model-fitting procedure to the subset. It evaluates how well the model fits the remaining data. It either accepts or rejects the current model. We will repeat the process until we find an acceptable model or the maximum number of iterations. The non-deterministic aspect of RANSAC is the random selection of the data points. We can use it for each iteration. We use it to automate cartography to identify the correct model parameters given a set of noisy data.

The RANSAC algorithm in scikit-learn does not have a direct way to access the inliers. But you can use the sklearn.linear_model.RANSACRegressor.inlier_mask_ property. It helps access an array of boolean values indicating. We can consider the samples according to the fitted model. The inlier_mask_ property is only available after the model has been fit. RANSAC can improve the accuracy of linear regression models. It eliminates outliers that may affect the model's accuracy.

In the RANSAC algorithm, the maybeInliers are points we can estimate part of the model. We usually identify the points. We can do it by computing a distance measure between the point and the model. If the distance is within a certain tolerance, we can consider the point a maybeInlier. We can use the maybeInliers to estimate the model parameters.

The median absolute deviation is a measure used in the RANSAC algorithm. It helps determine the quality of a model fit. We can calculate it by taking the median of the absolute deviations of the data points from the model. The MAD is useful for determining outliers in a dataset, as the outliers. Generally, it has higher MAD values than the non-outliers.

MLESAC is an improved version of RANSAC. It uses maximum likelihood estimation. It estimates the parameters of the model instead of the least squares estimation. This results in a more robust model fit and improved model performance. We can implement the RANSAC like Scikit-Learn, NumPy, OpenCV, SciPy, and RANSAC packages.

### Some tips for using RANSAC algorithms:

• Understand the algorithm parameters:

Take the time to understand the parameters and how they will affect the trades. We should tailor your algorithm to your trading strategy and risk tolerance.

• Test the algorithm before using it:

Before using it for live trading, testing it in a simulated environment is important. This allows you to ensure that the algorithm is working as expected. It won't cause any unexpected losses.

Monitoring the trades is important once the algorithm is set up and running. This allows you to ensure that the algorithm performs as expected and does not take too much risk.

When you feel comfortable, then executing the trades will be easy. Depending on the specified parameters will become vital. This ensures that we execute the trades with the correct parameters.

Regularly review the performance of your algorithm and adjust as necessary. This allows you to ensure that the algorithm performs as expected. It means that any parameter changes have the desired effect.

### Unique aspects of RANSAC technology in finance are:

• High Robustness:

RANSAC (Random Sample Consensus) technology is robust against outliers. We can use it to identify financial trends and anomalies in large datasets.

• Accurate Predictions:

RANSAC technology can predict future financial trends based on historical data.

• Automated Risk Management:

RANSAC technology can automate risk management processes by identifying and mitigating potential risks. We can do it before they become a major problem.

• Automated Portfolio Management:

RANSAC technology can automate portfolio management processes. We can do it by optimizing capital allocation across different asset classes.

• Fraud Detection:

RANSAC technology can detect financial frauds and anomalies in large datasets.

Fig 1: Preview of the Code and the Output.

### Code

In this solution, we are using RANSAC algorithm for Robust Regression in scikit-learn Python

### Instructions

Follow the steps carefully to get the output easily.

1. Install Jupyter Notebook on your computer.
2. Open terminal and install the required libraries with following commands.
3. Install sklearn by using the command: pip install sklearn.
4. Install numpy by using the command: pip install numpy.
5. Copy the code using the "Copy" button above and paste it into your IDE's Python file.
6. Run the file.

I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.

I found this code snippet by searching for "How to use RANSAC algorithm for Robust Regression in scikit-learn Python" in kandi. You can try any such use case!

### Dependent Libraries

numpyby numpy

Python 23755 Version:v1.25.0rc1

The fundamental package for scientific computing with Python.

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numpyby numpy

Python 23755 Version:v1.25.0rc1 License: Permissive (BSD-3-Clause)

The fundamental package for scientific computing with Python.
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scikit-learnby scikit-learn

Python 54584 Version:1.2.2

scikit-learn: machine learning in Python

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scikit-learnby scikit-learn

Python 54584 Version:1.2.2 License: Permissive (BSD-3-Clause)

scikit-learn: machine learning in Python
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If you do not have scikit-learn that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the scikit-learn page in kandi.

You can search for any dependent library on kandi like scikit-learn

### FAQ

What is RANSAC, and how does it work?

RANSAC (Random Sample Consensus) is an iterative algorithm. It helps estimate a parameter from a set of observed data containing outliers. It selects a subset of data points (inliers) that conform to a specific model. It then fits the model to the data points until we find the best fit. The algorithm then identifies outliers and discards them. The result is a robust model for outlier data points. It means we can fit the model despite outliers.

How is the new robust estimator of RANSAC different from traditional methods?

The new robust estimator of RANSAC is a more efficient and accurate algorithm. It helps identify outliers and estimate the parameters from datasets with outliers. Unlike traditional methods, RANSAC doesn't need the data to be completely outlier free. Instead, it uses a sampling approach to identify and discard outliers. It helps allow it to be more tolerant of noisy data. It also uses an iterative process to identify the best estimate of the model parameters. So it will increase its accuracy. Finally, it is more efficient than traditional methods. Since it only needs to sample a subset of the data to identify and discard the outliers.

How does RANSAC deal with outlier data?

RANSAC (Random Sample Consensus) is an iterative algorithm. It uses a subset of the dataset to fit a model. Then tests, the remaining data against the model to identify outliers. It will then discard the outliers and refit the model with the remaining inliers. RANSAC is useful when dealing with datasets that have a large number of outliers.

Explain the concept of Random sample consensus (RANSAC) in detail.

Random Sample Consensus (RANSAC) is an iterative algorithm. It helps to estimate the mathematical model parameters from a set of observed data. It contains outliers. It works by choosing a subset of the data points and fitting a model. We can test this model against the remaining data points. We can accept if the model is valid for a large part of the data points. We can return its parameters as the estimated solution.

We can use the RANSAC to estimate parameters for a model of a physical phenomenon. It can be the position of a camera in a 3D space or the parameters of a linear regression line. It is useful in cases where the data contains outliers since it is robust to the presence of outliers. RANSAC is useful when the data is subject to noise and errors. It is less sensitive to small errors than other estimation techniques.

RANSAC is an iterative algorithm. It requires several iterations until we find a satisfactory parameter estimate. Each iteration chooses a subset of the data points, and we must fit a model to this subset. The model is then tested against the remaining data points to determine if the model fits well to a large part.

What is the relationship between Random Forest and RANSAC algorithm?

Random Forest and RANSAC are machine learning algorithms. But we can use them for different purposes. Random Forest is an ensemble learning method. We can use it for classification and regression problems. At the same time, RANSAC is an iterative method. It helps to estimate the mathematical model parameters from a set of observed data. It contains outliers. We can use the Random Forest for supervised learning. But we use RANSAC for unsupervised learning.

How can we improve the image geometry using the RANSAC algorithm in Python?

RANSAC is an iterative algorithm. It helps estimate the mathematical model parameters from data containing outliers. We can improve the image geometry using this algorithm. We can do it by fitting a mathematical model to a dataset. The dataset can contain points from both images and remove any outliers. This will ensure the estimated parameters of the model. They are more robust and less susceptible to outliers.

We can implement the RANSAC algorithm using the RANSACRegressor class from Scikit-learn. This class implements a variety of RANSAC variants, including the basic RANSAC algorithm. Once the model fits the data, it can transform one image into another. In turn, it will improve the image geometry.

What measures can you calculate the mean absolute error for a given data set? With the Python implementation of the RANSAC algorithm?

• Define a function that computes the mean absolute error (MAE) between two data sets. This function should take the two data sets as inputs. It will then return a single scalar value representing the MAE between them.
• Split the input data into two subsets. We can use one subset to generate the model, while we should use the other to validate it.
• Use the RANSAC algorithm to generate a model that best fits the input data.
• Use the model to predict the output values for the validation subset.
• Calculate the MAE between the predicted and actual values for the validation subset.
• Return the MAE value.

What are some considerations when implementing a RANSAC Algorithm given a data set?

• Define the model:

Before implementing RANSAC, you will need to define the model of the data set. This includes the parameters, the equation, and the type of data.

• Decide on the number of data points required to fit the model:

RANSAC requires a certain number of data points to fit the model. You must decide on the appropriate number of data points before implementing RANSAC.

• Define the maximum number of iterations:

RANSAC requires a maximum number of iterations which defines how many times it will run. You need to decide on the appropriate number of iterations before implementing RANSAC.

• Define the threshold for inliers:

RANSAC requires a threshold that defines how close the data points should be to the model. You should decide on the appropriate threshold before implementing RANSAC.

• Set a random seed:

RANSAC requires a random seed for the algorithm to generate random numbers. You should decide on the appropriate seed before implementing RANSAC.

• Set up the algorithm:

Once all the above considerations, you can set up the RANSAC algorithm. This includes setting up the variables, defining the functions, and writing the code.

Provide an example code to demonstrate how to use the Ransac Algorithm in Python.

import numpy as np

from sklearn.linear_model import RANSACRegressor

# Create some random data

x = np.random.rand(200,1)

y = 0.5*x*x + x + np.random.rand(200,1)

# Fit line using all data

model = RANSACRegressor()

model.fit(x, y)

# Robustly fit a linear model with the RANSAC algorithm

model_ransac = RANSACRegressor(min_samples=2, residual_threshold=5.0)

model_ransac.fit(x, y)

# Predict data of estimated models

line_y_ransac = model_ransac.predict(x)

# Plot results

plt.scatter(x, y, c='b')

plt.plot(x, line_y_ransac, c='r')

plt.show()

Can you describe the best practices for the implementation of the Ransac Algorithm?

• Use a higher-order data type (e.g., a class) to represent a model instance and its associated parameters. This will help keep the code clean and organized.
• Use NumPy for efficient operations on large data sets.
• Use Visualization methods to understand the data better. Then the results of the Ransac algorithm.
• Make sure to include unit tests for every implementation component. We can be sure that the code works as we expect.
• Ensure to include logging of intermediate and final results to identify problems better.
• Use multiprocessing to speed up computations.
• Ensure to include a setting for the maximum number of iterations of Ransac. It will help prevent infinite loops.
• Use a random seed when running the algorithm to ensure we produce the same results each time.

### Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

1. The solution is created in Python 3.9.6
2. The solution is tested on numpy version 1.21.4
3. The solution is tested on sklearn version 1.1.3

Using this solution, we are able to use RANSAC algorithm for Robust Regression in scikit-learn Python.

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