Regression methods play an important role in aviation by enabling the prediction of variables like fuel consumption, flight times, and maintenance needs. Different regression techniques can be used, including linear regression, ridge regression, and lasso regression. Key considerations for applying regression in aviation include feature selection, addressing multicollinearity, and selecting the appropriate model. Regression analysis has various applications and can help optimize aspects of aviation operations and management.
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linear regression is a linear approach for modelling a predictive relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables), which are measured without error. The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable. If the explanatory variables are measured with error then errors-in-variables models are required, also known as measurement error models.
In linear regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. Such models are called linear models. Most commonly, the conditional mean of the response given the values of the explanatory variables (or predictors) is assumed to be an affine function of those values; less commonly, the conditional median or some other quantile is used. Like all forms of regression analysis, linear regression focuses on the conditional probability distribution of the response given the values of the predictors, rather than on the joint probability distribution of all of these variables, which is the domain of multivariate analysis.
Linear regression was the first type of regression analysis to be studied rigorously, and to be used extensively in practical applications.[4] This is because models which depend linearly on their unknown parameters are easier to fit than models which are non-linearly related to their parameters and because the statistical properties of the resulting estimators are easier to determine.
Linear regression has many practical uses. Most applications fall into one of the following two broad categories:
If the goal is error reduction in prediction or forecasting, linear regression can be used to fit a predictive model to an observed data set of values of the response and explanatory variables. After developing such a model, if additional values of the explanatory variables are collected without an accompanying response value, the fitted model can be used to make a prediction of the response.
If the goal is to explain variation in the response variable that can be attributed to variation in the explanatory variables, linear regression analysis can be applied to quantify the strength of the relationship between the response and the explanatory variables, and in particular to determine whether some explanatory variables may have no linear relationship with the response at all, or to identify which subsets of explanatory variables may contain redundant information about the response.
This presentation educates you about Linear Regression, SPSS Linear regression, Linear regression method, Why linear regression is important?, Assumptions of effective linear regression and Linear-regression assumptions.
For more topics stay tuned with Learnbay.
Performance analysis of regularized linear regression models for oxazolines a...ijcsity
Regularized regression technique
s for lin
ear regression have been creat
ed
the last few
ten
year
s to
reduce
the
flaws
of ordinary least squ
ares regression
with regard to prediction accuracy.
In this paper, new
methods
for using regularized regression in model
choice are introduc
ed, and we
distinguish
the condition
s
in whic
h regularized regression develops
our ability to discriminate models.
W
e applied all the five
methods that use penalty
-
based (regularization) shrinkage to
handle Oxazolines and Oxazoles derivatives
descriptor
dataset
with far more predictors than observations.
The lasso,
ridge,
elasticnet,
lars and relaxed
lasso
further pos
sess the desirable property that they simultaneously sele
ct relevant predictive descriptor
s
and optimally
estimate their effects.
Here, we comparatively evaluate the performance of five regularized
linear regression methods
The assessment of the performanc
e of each model by means of benchmark
experiments
is an
established exercise.
Cross
-
validation and
resampling
method
s
are genera
lly
used to
arrive
point
evaluates the efficienci
es which are compared
to recognize
methods
with acceptable feature
s.
Predictiv
e accuracy
was evaluated
us
ing the root mean squared error
(RMSE)
and
Square of usual
correlation between predictors and observed mean inhibitory concentration of antitubercular activity
(R
square)
.
We found that all five regularized regression models were
able to produce feasible models
and
efficient capturing the linearity in the data
.
The elastic net and lars had similar accuracies
as well as lasso
and relaxed lasso
had similar accuracies
but outperformed ridge regression in terms of the RMSE and R
squ
are
metrics.
Mixture Regression Model for Incomplete DataLoc Nguyen
The Regression Expectation Maximization (REM) algorithm, which is a variant of Expectation Maximization (EM) algorithm, uses parallelly a long regression model and many short regression models to solve the problem of incomplete data. Long regression model is entire regression function which is the resulted model and short regression models are partial regression functions which are inverses of entire regression function. I proposed REM in a different research in which an entire regression function is built parallelly with many partial inverse regression functions and then missing values are fulfilled by expectations relevant to both entire regression function and inverse regression functions. Experimental results proved resistance of REM to incomplete data, but accuracy of REM decreases insignificantly when data sample is made sparse with loss ratios up to 80%.
Like traditional regression analysis methods, accuracy of REM can be decreased if data varies complicatedly with many trends. In this research, I propose a so-called Mixture Regression Expectation Maximization (MREM) algorithm. MREM is full combination of REM and mixture model in which I use two EM processes in the same loop. MREM uses the first EM process for exponential family of probability distributions to estimate missing values as REM does. Consequently, MREM uses the second EM process to estimate parameters as mixture model method does. The purpose of MREM is to take advantages of both REM and mixture model. Unexpectedly, experimental result shows that MREM is less accurate than REM. I try to weight partial models of MREM by product of component probabilities and conditional probabilities or to select most appropriate partial model in order to improve estimation accuracy, but the final results are not as good as expected. However, MREM is essential because a different approach for mixture model can be referred by fusing linear equations of MREM into a unique curve equation proposed by some other researches.
linear regression is a linear approach for modelling a predictive relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables), which are measured without error. The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable. If the explanatory variables are measured with error then errors-in-variables models are required, also known as measurement error models.
In linear regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. Such models are called linear models. Most commonly, the conditional mean of the response given the values of the explanatory variables (or predictors) is assumed to be an affine function of those values; less commonly, the conditional median or some other quantile is used. Like all forms of regression analysis, linear regression focuses on the conditional probability distribution of the response given the values of the predictors, rather than on the joint probability distribution of all of these variables, which is the domain of multivariate analysis.
Linear regression was the first type of regression analysis to be studied rigorously, and to be used extensively in practical applications.[4] This is because models which depend linearly on their unknown parameters are easier to fit than models which are non-linearly related to their parameters and because the statistical properties of the resulting estimators are easier to determine.
Linear regression has many practical uses. Most applications fall into one of the following two broad categories:
If the goal is error reduction in prediction or forecasting, linear regression can be used to fit a predictive model to an observed data set of values of the response and explanatory variables. After developing such a model, if additional values of the explanatory variables are collected without an accompanying response value, the fitted model can be used to make a prediction of the response.
If the goal is to explain variation in the response variable that can be attributed to variation in the explanatory variables, linear regression analysis can be applied to quantify the strength of the relationship between the response and the explanatory variables, and in particular to determine whether some explanatory variables may have no linear relationship with the response at all, or to identify which subsets of explanatory variables may contain redundant information about the response.
This presentation educates you about Linear Regression, SPSS Linear regression, Linear regression method, Why linear regression is important?, Assumptions of effective linear regression and Linear-regression assumptions.
For more topics stay tuned with Learnbay.
Performance analysis of regularized linear regression models for oxazolines a...ijcsity
Regularized regression technique
s for lin
ear regression have been creat
ed
the last few
ten
year
s to
reduce
the
flaws
of ordinary least squ
ares regression
with regard to prediction accuracy.
In this paper, new
methods
for using regularized regression in model
choice are introduc
ed, and we
distinguish
the condition
s
in whic
h regularized regression develops
our ability to discriminate models.
W
e applied all the five
methods that use penalty
-
based (regularization) shrinkage to
handle Oxazolines and Oxazoles derivatives
descriptor
dataset
with far more predictors than observations.
The lasso,
ridge,
elasticnet,
lars and relaxed
lasso
further pos
sess the desirable property that they simultaneously sele
ct relevant predictive descriptor
s
and optimally
estimate their effects.
Here, we comparatively evaluate the performance of five regularized
linear regression methods
The assessment of the performanc
e of each model by means of benchmark
experiments
is an
established exercise.
Cross
-
validation and
resampling
method
s
are genera
lly
used to
arrive
point
evaluates the efficienci
es which are compared
to recognize
methods
with acceptable feature
s.
Predictiv
e accuracy
was evaluated
us
ing the root mean squared error
(RMSE)
and
Square of usual
correlation between predictors and observed mean inhibitory concentration of antitubercular activity
(R
square)
.
We found that all five regularized regression models were
able to produce feasible models
and
efficient capturing the linearity in the data
.
The elastic net and lars had similar accuracies
as well as lasso
and relaxed lasso
had similar accuracies
but outperformed ridge regression in terms of the RMSE and R
squ
are
metrics.
Mixture Regression Model for Incomplete DataLoc Nguyen
The Regression Expectation Maximization (REM) algorithm, which is a variant of Expectation Maximization (EM) algorithm, uses parallelly a long regression model and many short regression models to solve the problem of incomplete data. Long regression model is entire regression function which is the resulted model and short regression models are partial regression functions which are inverses of entire regression function. I proposed REM in a different research in which an entire regression function is built parallelly with many partial inverse regression functions and then missing values are fulfilled by expectations relevant to both entire regression function and inverse regression functions. Experimental results proved resistance of REM to incomplete data, but accuracy of REM decreases insignificantly when data sample is made sparse with loss ratios up to 80%.
Like traditional regression analysis methods, accuracy of REM can be decreased if data varies complicatedly with many trends. In this research, I propose a so-called Mixture Regression Expectation Maximization (MREM) algorithm. MREM is full combination of REM and mixture model in which I use two EM processes in the same loop. MREM uses the first EM process for exponential family of probability distributions to estimate missing values as REM does. Consequently, MREM uses the second EM process to estimate parameters as mixture model method does. The purpose of MREM is to take advantages of both REM and mixture model. Unexpectedly, experimental result shows that MREM is less accurate than REM. I try to weight partial models of MREM by product of component probabilities and conditional probabilities or to select most appropriate partial model in order to improve estimation accuracy, but the final results are not as good as expected. However, MREM is essential because a different approach for mixture model can be referred by fusing linear equations of MREM into a unique curve equation proposed by some other researches.
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Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
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Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
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Antifertility, Toxicity studies as per OECD guidelines
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
TESDA TM1 REVIEWER FOR NATIONAL ASSESSMENT WRITTEN AND ORAL QUESTIONS WITH A...
ml-05x01.pdf
1. This course is prepared under the Erasmus+ KA-210-YOU Project titled
«Skilling Youth for the Next Generation Air Transport Management»
Machine Learning
Applications in Aviation
Regression Methods
Asst. Prof. Dr. Emircan Özdemir
Eskişehir Technical University
2. • Regression Methods play a crucial role in predicting numerical outcomes
in the context of machine learning.
• In aviation industry, forecasting fuel consumption, optimizing flight paths,
predicting maintenance needs, anticipating passenger demand for a
particular route, optimizing marketing strategies for increased ticket sales,
or predicting customer satisfaction levels, regression methods offer a
versatile toolkit for modeling intricate relationships between variables in
the aviation domain.
Regression Methods 2
Introduction
3. • Simple Linear Regression
Simple Linear Regression is a foundational regression method used in machine learning
and statistics to model the relationship between two variables: one independent variable
(predictor) and one dependent variable (outcome). The goal is to establish a linear
equation that best fits the observed data points, allowing for predictions or estimations of
the dependent variable based on changes in the independent variable.
The equation of a simple linear regression line is typically expressed as:
Regression Methods 3
Types of Regression Problems
4. • Simple Linear Regression
The regression analysis aims to find the values of b0 and b1 that minimize the difference
between the observed and predicted values, often using methods like the least squares
approach.
Regression Methods 4
Types of Regression Problems
Source: https://medium.com/@sachin.hs20/simple-linear-regression-using-example-e4e2a89df54c
5. • Simple Linear Regression
In aviation, this model could help predict fuel consumption based on the number of
passengers, offering insights into how changes in passenger load may impact fuel
efficiency. This, in turn, aids in optimizing flight operations, resource planning, and cost
management.
Regression Methods 5
Types of Regression Problems
6. • Multiple Linear Regression
Multiple Linear Regression is an extension of Simple Linear Regression, allowing for the
modeling of the relationship between a dependent variable and multiple independent
variables. In this method, the goal is to create a linear equation that best fits the observed
data points by considering the impact of multiple predictors on the outcome variable.
The general form of the Multiple Linear Regression equation is:
Regression Methods 6
Types of Regression Problems
7. • Multiple Linear Regression
In the context of aviation, consider a scenario where the fuel consumption of an aircraft
(dependent variable) is influenced not only by the number of passengers (as in Simple
Linear Regression) but also by additional factors such as flight distance, weather conditions,
and aircraft type.
Multiple Linear Regression enables the creation of a model that incorporates all these
variables to provide a more comprehensive understanding of how they collectively influence
fuel consumption.
Regression Methods 7
Types of Regression Problems
Source: https://www.mathworks.com/help/stats/regress.html
8. • Polynomial Regression
Polynomial Regression is a type of regression analysis that extends the concept of linear
regression by introducing polynomial terms, allowing for the modeling of non-linear
relationships between the dependent and independent variables. In other words, it
accommodates situations where the relationship between the variables cannot be
adequately represented by a straight line.
Regression Methods 8
Types of Regression Problems
9. • Polynomial Regression
The Polynomial Regression equation takes the form:
Regression Methods 9
Types of Regression Problems
10. • Polynomial Regression
The choice of the degree of the polynomial (n) depends on the complexity of the
relationship in the data. A higher-degree polynomial can fit the data more closely but may
risk overfitting, capturing noise rather than true patterns.
In the context of aviation, Polynomial Regression can be applied when predicting variables
with non-linear trends.
For example, forecasting the relationship between aircraft altitude and fuel efficiency might
involve a polynomial model, as the impact of altitude on fuel efficiency may not follow a
straight line. Polynomial Regression allows for a more flexible representation of such
relationships, enhancing the accuracy of predictions in scenarios where non-linear patterns
are evident.
Regression Methods 10
Types of Regression Problems
11. • Linear Regression
It serves as the foundational algorithm for modeling linear relationships
between variables.
This method is particularly useful in scenarios where a clear, straight-line
relationship can be established, such as predicting fuel consumption based
on flight time.
Regression Methods 11
Popular Regression Algorithms
12. • Ridge Regression
It introduces regularization techniques to improve model performance.
In aviation, where datasets often exhibit multicollinearity among variables,
Ridge Regression's regularization helps mitigate overfitting and enhances
the model's generalizability, contributing to more accurate predictions.
The aim of ridge regression is to minimize the sum of squared residuals (like
ordinary linear regression), but with an additional penalty for the sum of
squared coefficients (L2 regularization term).
It helps prevent overfitting, particularly when there is multicollinearity among
predictor variables.
Regression Methods 12
Popular Regression Algorithms
13. • Lasso Regression
Lasso Regression, on the other hand, brings in L1 regularization, offering a
unique advantage in feature selection.
This is crucial in aviation scenarios where selecting the most relevant
features can significantly impact the accuracy and efficiency of regression
models. Lasso Regression aids in identifying and prioritizing the key
variables that influence outcomes, such as predicting aircraft maintenance
needs.
Its aim is similar to Ridge Regression, but with an L1 regularization term,
which penalizes the absolute values of the coefficients.
It promotes sparsity in the coefficient values, effectively performing
automatic feature selection by pushing certain coefficients to exactly zero.
Regression Methods 13
Popular Regression Algorithms
14. • Elastic Net Regression
Elastic Net Regression is a hybrid regularization technique that combines
aspects of both Ridge Regression and Lasso Regression. It incorporates
both L1 (Lasso) and L2 (Ridge) regularization terms into the linear
regression objective function.
It aims to minimize the sum of squared residuals, like ordinary linear
regression, including both L1 and L2 regularization terms to penalize the
sum of absolute values (L1) and the sum of squared values (L2) of the
coefficients.
It’s suitable for situations where there are many correlated features, and
automatic feature selection is desired.
Regression Methods 14
Popular Regression Algorithms
15. • Building the right model is important to avoid overfitting and underfitting. In
In some cases, Ridge, Lasso and Elastic Net regression algorithms help
us to make better long term predictions and avoid over/under fitting.
Regression Methods 15
Overfitting and Underfitting
Source: https://www.geeksforgeeks.org/lasso-vs-ridge-vs-elastic-net-ml/
16. • Mean Squared Error (MSE)
MSE is a regression evaluation metric that quantifies the average squared difference
between predicted values and actual values in the dataset.
A lower MSE indicates better model performance, with smaller errors between predicted
and actual values. It provides a measure of the accuracy of the model's predictions.
Regression Methods 16
Regression Evaluation Metrics
17. • Root Mean Squared Error (RMSE)
The RMSE is a commonly used metric for evaluating the accuracy of a regression model. It
represents the square root of the average squared differences between the actual and
predicted values.
The lower the RMSE, the better the model's performance, as it indicates smaller prediction
errors.
Regression Methods 17
Regression Evaluation Metrics
18. • Mean Absolute Error (MAE)
MAE is another metric for evaluating the accuracy of a regression model. It measures the
average absolute differences between the actual and predicted values.
Unlike the squared differences in RMSE, MAE provides a more straightforward
interpretation of the average prediction error.
The lower the MAE, the better the model's performance in terms of absolute prediction
accuracy.
Regression Methods 18
Regression Evaluation Metrics
19. • R-squared (𝑹𝟐)
R-squared is a statistical metric that represents the proportion of the variance in the
dependent variable that is predictable from the independent variables. It ranges from 0 to 1,
where 1 indicates a perfect fit.
A higher R-squared value signifies a better fit of the regression model to the data. It
quantifies the proportion of variability in the dependent variable that can be explained by the
independent variables. However, caution is needed, as high R-squared does not
necessarily imply causation.
Regression Methods 19
Regression Evaluation Metrics
20. Conclusion:
• The lower value of MAE, MSE, and RMSE implies higher accuracy of a
regression model.
• However, a higher value of R-squared is considered desirable.
Regression Methods 20
Regression Evaluation Metrics
21. • Predictive Maintenance
Regression is employed to forecast maintenance needs based on historical data. By
analyzing patterns and trends in past maintenance records, aviation companies can
proactively schedule maintenance activities, reducing the risk of unplanned downtime and
improving overall operational efficiency.
• Predictors: Historical maintenance records, Sensor data from aircraft components, Flight
hours and cycles
• Outcome(s): Predicted time until the next maintenance event, Identification of specific
components requiring attention
Regression Methods 21
Regression Use Cases in Aviation
22. • Fuel Consumption Optimization
Regression models play a crucial role in optimizing fuel efficiency in aviation. By considering
factors such as aircraft type, weather conditions, and flight routes, regression helps in
developing models that guide fuel consumption strategies. This contributes to cost savings
and environmental sustainability.
• Predictors: Aircraft type and mode, Weather conditions, Flight route and altitude
• Outcome(s): Optimal fuel consumption rate, Cost-effective fuel usage strategies
Regression Methods 22
Regression Use Cases in Aviation
23. • Flight Time Prediction
Regression is utilized for estimating flight durations and enhancing scheduling processes.
By analyzing various variables such as departure and arrival locations, historical data, and
potential delays, regression models assist in predicting more accurate flight times,
improving overall airline scheduling efficiency.
• Predictors: Departure and arrival locations, Historical flight data Weather forecasts
• Outcome(s): Predicted flight duration, More accurate scheduling of arrival and departure
times
Regression Methods 23
Regression Use Cases in Aviation
24. • Passenger Loyalty Prediction
Airlines can leverage regression methods to analyze historical data related to passenger
behavior, including factors like booking frequency, travel preferences, and demographic
information. By building regression models, airlines can predict the likelihood of passengers
remaining loyal to their services. This allows for targeted strategies to enhance passenger
experience, offer personalized incentives, and ultimately foster stronger customer loyalty in
the highly competitive aviation industry.
• Predictors: Historical passenger behavior, Booking frequency, Travel preferences and
patterns, Demographic information
• Outcome(s): Predicted likelihood of a passenger choosing the airline for future travels,
Identification of factors influencing passenger loyalty
Regression Methods 24
Regression Use Cases in Aviation
25. • Feature Selection
The selection of features plays a pivotal role. Features are the variables
used to predict the outcome, and choosing relevant ones is crucial for
accurate regression models.
Aviation datasets may contain numerous variables, and careful
consideration is needed to identify which features significantly contribute to
the predictive power of the model.
Feature selection ensures that the chosen variables align with the specific
objectives of the regression task, enhancing the model's efficacy and
interpretability.
Regression Methods 25
Considerations and Challenges
26. • Multicollinearity
Multicollinearity refers to the presence of correlated predictor variables in a
regression model. In aviation datasets, certain features may exhibit high
correlation, potentially posing challenges.
When predictor variables are highly correlated, it becomes difficult to
distinguish the individual impact of each variable on the outcome.
Addressing multicollinearity is essential for maintaining the stability and
reliability of regression models.
Techniques such as variance inflation factor (VIF) analysis can be employed
to identify and mitigate multicollinearity, ensuring the robustness of the
regression analysis in aviation applications.
Regression Methods 26
Considerations and Challenges
27. • Data Preprocessing
Data preprocessing is a critical step in aviation regression, emphasizing the
importance of cleaning and preparing data before applying regression
models.
Aviation datasets can be complex, containing various variables with missing
values, outliers, or inconsistencies. Data preprocessing techniques involve
handling missing data, addressing outliers, and transforming variables to
ensure they align with the assumptions of regression models.
By cleaning and preparing the data meticulously, analysts create a robust
foundation for regression analysis, enhancing the accuracy and reliability of
the subsequent modeling.
Regression Methods 27
Best Practices
28. • Model Selection
Model selection is a key facet of successful aviation regression. Different
regression algorithms may be suited to varying aviation problems, and
selecting the right algorithm is crucial for achieving accurate and meaningful
results.
Considerations such as the nature of the data, the relationship between
variables, and the specific goals of the regression task guide the choice of
regression algorithm. Whether it's linear regression, ridge regression, or
other advanced techniques, thoughtful model selection ensures that the
regression model aligns optimally with the characteristics of the aviation
dataset and the objectives of the analysis.
Regression Methods 28
Best Practices
29. • Ensemble Regression Models
Ensemble regression models represent a future trend in aviation that
involves the exploration of combining multiple regression models for
enhanced accuracy and robust predictions.
Ensemble methods, such as Random Forest or Gradient Boosting, leverage
the strength of diverse models to mitigate individual weaknesses and
improve overall performance. By aggregating predictions from multiple
models, ensemble techniques offer a sophisticated approach to handle
complex relationships within aviation datasets, contributing to more reliable
regression outcomes.
Regression Methods 29
Future Trends
30. • Explainable AI in Regression:
The evolving trend of Explainable AI in regression underscores the
increasing importance of transparency and interpretability in regression
models. As regression techniques become more advanced and complex,
understanding how models arrive at specific predictions becomes crucial,
especially in aviation scenarios where decisions impact safety and
operational efficiency. Explainable AI in regression aims to demystify the
decision-making process of advanced models, providing insights into the
factors influencing predictions and fostering trust in the outcomes. This trend
aligns with the industry's growing emphasis on accountability and
comprehension of AI-driven regression analyses.
Regression Methods 30
Future Trends
31. • In RapidMiner, using the Repository window, follow the path
Training Resources-Model-Supervised-Linear Regression
and open the Hotel App CLV Linear Regression solution
process.
• In this example, the outcome (label) variable is «Customer
Lifetime Value (CLV)».
• CLV is the total revenue or profit generated by a customer
over the entire course of their relationship with your business.
• Predicting CLV using regression is of paramount importance
in the field of business and marketing.
• Since CLV variable is also realted with airline business, so
we will use this regression example.
Regression Methods 31
RapidMiner Example on Linear Regression Model
32. • In the process window of this model, you can see main operators for data importing, cross
validation. The cross validation operator also includes the model operators (train and
test), data split function and performance operator.
Regression Methods 32
RapidMiner Example on Linear Regression Model
33. • When you double-click the cross validation operator, you can see two main areas for
training and testing. You can simply drag and drop model and performance operators in
these areas.
Regression Methods 33
RapidMiner Example on Linear Regression Model
34. Cross Validation is a technique used for assessing the performance and generalization ability of a
predictive model. Cross-validation helps to evaluate how well a model will perform on an independent
dataset by partitioning the available data into subsets. The basic idea is to train and test the model
multiple times on different subsets of the data, and then average the performance metrics to get a
more reliable estimate of the model's performance. This operator:
• Splits data into k subsets (folds), typically with a value of k such as 5 or 10.
• The model is trained on k-1 folds and tested on the remaining fold. This process is repeated k
times, each time using a different fold as the test set.
• The performance metrics are recorded for each iteration.
• The average performance across all iterations is calculated to provide a more robust estimate of the
model's performance.
• You can simply arrange «number of folds (k)» and «sampling type» using the operator parameters
window on the left.
Regression Methods 34
RapidMiner Example on Linear Regression Model
35. • After you run the model,
you can find the outputs
in the Results view.
• You can find coefficients
of each predictor and p-
values of these
variables in the
regression model
equation.
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RapidMiner Example on Linear Regression Model
36. • You can find the values of the performance indicators like MSE, RMSE, R-squared etc.
Also, please keep in mind that you can get more performance indicators choosing the
options in the performance operator.
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RapidMiner Example on Linear Regression Model
37. • You can find the predictions for each row in the Results view. These prediction data can
be used for futher visualization or discussion.
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RapidMiner Example on Linear Regression Model
38. • For example you can create graphs using the visualizations tab in the left menu options.
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RapidMiner Example on Linear Regression Model
39. After running the regression model you can:
• Explain the relationship between variables.
• Obtain predicted values for the label attribute.
• Get performance metrics for the model and interpret.
• Visualize the outputs.
• Get statistical outputs for the model.
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RapidMiner Example on Linear Regression Model
40. • In conclusion, this chapter provides a comprehensive exploration of
Regression Methods in aviation.
• With a solid understanding of predictive modeling, regression models
offers a robust toolkit for predicting numerical outcomes and optimizing
various aspects within the aviation industry.
• In order to enhance your proficiency in leveraging regression techniques
for nuanced problem-solving in aviation analytics, not only grasp these
foundational concepts but to actively apply them in practical scenarios
using RapidMiner.
Regression Methods 40
Conclusion