Use a combination of algorithms

Oct 13, 2025 by pynkocean.com 20 views

I Can't Get My R² Above 70%: A Deep Dive into Machine Learning

As a machine learning enthusiast, achieving a high R² value is often a benchmark for the success of a model. However, when the R² value remains stubbornly below 70%, it can be frustrating and demotivating. In this article, we will delve into the world of machine learning and explore the possible reasons behind this issue. We will discuss various algorithms, techniques, and strategies that can help improve the R² value and push it above the 70% mark.

Before we dive into the solutions, let's first understand what R² is and why it's an important metric in machine learning. R², also known as the coefficient of determination, measures the proportion of the variance in the dependent variable that is predictable from the independent variable(s). In other words, it measures how well the model fits the data. A high R² value indicates that the model is able to explain a large portion of the variance in the data, while a low R² value suggests that the model is not able to capture the underlying patterns in the data.

You have already tried several algorithms, including RandomForest, LGBM, KNeighbors, and Polynomial Regression. Let's take a closer look at each of these algorithms and explore their strengths and weaknesses.

RandomForest

RandomForest is an ensemble learning method that combines multiple decision trees to improve the accuracy and robustness of the model. It works by creating multiple decision trees on random subsets of the data and then combining their predictions to produce a final output. RandomForest is a popular choice for many machine learning tasks, including regression and classification problems.

However, RandomForest can suffer from overfitting, especially when the number of features is large. This can lead to a decrease in the R² value. To mitigate this issue, you can try the following:

Feature selection: Select a subset of the most relevant features to reduce the dimensionality of the data and prevent overfitting.
Hyperparameter tuning: Tune the hyperparameters of the RandomForest model, such as the number of trees and the maximum depth, to optimize its performance.
Ensemble methods: Combine RandomForest with other ensemble methods, such as Gradient Boosting or AdaBoost, to improve its performance.

LGBM

LGBM (Light Gradient Boosting Machine) is another popular ensemble learning method that combines multiple decision trees to improve the accuracy and robustness of the model. It works by creating multiple decision trees on random subsets of the data and then combining their predictions to produce a final output. LGBM is known for its high performance and interpretability.

However, LGBM can suffer from overfitting, especially when the number of features is large. This can lead to a decrease in the R² value. To mitigate this issue, you can try the following:

Feature selection: Select a subset of the most relevant features to reduce the dimensionality of the data and prevent overfitting.
Hyperparameter tuning: Tune the hyperparameters of the LGBM model, such as the number of leaves and the learning rate, to optimize its performance.
Ensemble methods: Combine LGBM with other ensemble methods, such as RandomForest or Gradient Boosting, to improve its performance.

KNeighbors

KNeighbors is a simple yet effective algorithm that works by finding the k-nearest neighbors to a given data point and using their values to make a prediction. KNeighbors is a popular choice for many machine learning tasks, including regression and classification problems.

However, KNeighbors can suffer from overfitting, especially when the number of features is large. This can lead to a decrease in the R² value. To mitigate this issue, you can try the following:

Feature selection: Select a subset of the most relevant features to reduce the dimensionality of the data and prevent overfitting.
Hyperparameter tuning: Tune the hyperparameters of the KNeighbors model, such as the number of neighbors and the distance metric, to optimize its performance.
Ensemble methods: Combine KNeighbors with other ensemble methods, such as RandomForest or Gradient Boosting, to improve its performance.

Polynomial Regression

Polynomial Regression is a type of regression analysis that uses a polynomial function to model the relationship between the independent and dependent variables. It works by fitting a polynomial curve to the data and using the coefficients of the polynomial to make predictions.

However, Polynomial Regression can suffer from overfitting, especially when the degree of the polynomial is high. This can lead to a decrease in the R² value. To mitigate this issue, you can try the following:

Feature selection: Select a subset of the most relevant features to reduce the dimensionality of the data and prevent overfitting.
Hyperparameter tuning: Tune the hyperparameters of the Polynomial Regression model, such as the degree of the polynomial and the regularization strength, to optimize its performance.
Ensemble methods: Combine Polynomial Regression with other ensemble methods, such as RandomForest or Gradient Boosting, to improve its performance.

Data preprocessing is an essential step in machine learning that involves cleaning, transforming, and preparing the data for modeling. In this section, we will discuss some common data preprocessing techniques that can help improve the R² value.

Handling Missing Values

Missing values can significantly impact the performance of a machine learning model. To handle missing values, you can try the following:

Imputation: Impute missing values using the mean, median, or mode of the respective feature.
Interpolation: Interpolate missing values using a linear or polynomial function.
Deletion: Delete rows or columns with missing values.

Scaling and Normalization

Scaling and normalization are essential steps in machine learning that involve transforming the data to have a similar scale and distribution. To scale and normalize the data, you can try the following:

Standard Scaler: Use the Standard Scaler to scale the data to have a mean of 0 and a standard deviation of 1.
MinMax Scaler: Use the MinMax Scaler to scale the data to have a minimum value of 0 and a maximum value of 1.
Robust Scaler: Use the Robust Scaler to scale the data using the interquartile range (IQR) instead of the standard deviation.

Feature Engineering

Feature engineering is the process of creating new features from existing ones to improve the performance of a machine learning model. To engineer new features, you can try the following:

Polynomial features: Create polynomial features by raising the existing features to a power.
Interaction features: Create interaction features by multiplying the existing features together.
Transformations: Apply transformations, such as logarithmic or exponential, to the existing features.

Achieving a high R² value is a challenging task in machine learning, but it's not impossible. By trying different algorithms, techniques, and strategies, you can improve the R² value and push it above the 70% mark. Remember to always evaluate the performance of your model using metrics such as R², mean squared error (MSE), and mean absolute error (MAE). With practice and patience, you can become a skilled machine learning practitioner and achieve high R² values in your models.

Here are some additional tips and tricks that can help you improve the R² value:

Use a combination of algorithms: Combine multiple algorithms, such as RandomForest and LGBM, to improve the performance of your model.
Use ensemble methods: Use ensemble methods, such as bagging and boosting, to improve the performance of your model.
Use feature selection: Use feature selection techniques, such as recursive feature elimination (RFE), to select the most relevant features for your model.
Use hyperparameter tuning: Use hyperparameter tuning techniques, such as grid search and random search, to optimize the hyperparameters of your model.
Use cross-validation: Use cross-validation techniques, such as k-fold cross-validation, to evaluate the performance of your model on unseen data.

By following these tips and tricks, you can improve the R² value and achieve high performance in your machine learning models.
I Can't Get My R² Above 70%: A Q&A Article

As a machine learning enthusiast, achieving a high R² value is often a benchmark for the success of a model. However, when the R² value remains stubbornly below 70%, it can be frustrating and demotivating. In this article, we will answer some common questions related to improving the R² value and provide tips and tricks to help you achieve high performance in your machine learning models.

A: There are several reasons why your R² value may be low. Some common reasons include:

Overfitting: When a model is too complex and fits the training data too closely, it can lead to overfitting and a low R² value.
Underfitting: When a model is too simple and fails to capture the underlying patterns in the data, it can lead to underfitting and a low R² value.
Insufficient data: When the amount of data is limited, it can lead to a low R² value.
Poor feature selection: When the features used in the model are not relevant or are highly correlated, it can lead to a low R² value.

A: There are several ways to prevent overfitting in your model, including:

Regularization: Regularization techniques, such as L1 and L2 regularization, can help prevent overfitting by adding a penalty term to the loss function.
Early stopping: Early stopping can help prevent overfitting by stopping the training process when the model starts to overfit.
Data augmentation: Data augmentation can help prevent overfitting by artificially increasing the size of the training dataset.
Ensemble methods: Ensemble methods, such as bagging and boosting, can help prevent overfitting by combining the predictions of multiple models.

A: There are several ways to select the best features for your model, including:

Recursive feature elimination (RFE): RFE is a feature selection technique that recursively eliminates the least important features until a specified number of features is reached.
Mutual information: Mutual information is a feature selection technique that measures the mutual information between each feature and the target variable.
Correlation analysis: Correlation analysis is a feature selection technique that measures the correlation between each feature and the target variable.
Permutation importance: Permutation importance is a feature selection technique that measures the importance of each feature by permuting the values of the feature and measuring the decrease in the model's performance.

A: There are several ways to tune the hyperparameters of your model, including:

Grid search: Grid search is a hyperparameter tuning technique that searches through a grid of possible hyperparameter values to find the best combination.
Random search: Random search is a hyperparameter tuning technique that randomly samples the hyperparameter space to find the best combination.
Bayesian optimization: Bayesian optimization is a hyperparameter tuning technique that uses a probabilistic model to search for the best combination of hyperparameters.
Gradient-based optimization: Gradient-based optimization is a hyperparameter tuning technique that uses gradient-based optimization algorithms to search for the best combination of hyperparameters.

A: There are several ways to evaluate the performance of your model, including:

Mean squared error (MSE): MSE is a metric that measures the average squared difference between the predicted and actual values.
Mean absolute error (MAE): MAE is a metric that measures the average absolute difference between the predicted and actual values.
R-squared (R²): R² is a metric that measures the proportion of the variance in the dependent variable that is predictable from the independent variable(s).
Cross-validation: Cross-validation is a technique that evaluates the performance of a model on unseen data by splitting the data into training and testing sets.

Improving the R² value is a challenging task in machine learning, but it's not impossible. By trying different algorithms, techniques, and strategies, you can improve the R² value and push it above the 70% mark. Remember to always evaluate the performance of your model using metrics such as R², MSE, and MAE. With practice and patience, you can become a skilled machine learning practitioner and achieve high R² values in your models.

Here are some additional tips and tricks that can help you improve the R² value:

Use a combination of algorithms: Combine multiple algorithms, such as RandomForest and LGBM, to improve the performance of your model.
Use ensemble methods: Use ensemble methods, such as bagging and boosting, to improve the performance of your model.
Use feature selection: Use feature selection techniques, such as RFE, to select the most relevant features for your model.
Use hyperparameter tuning: Use hyperparameter tuning techniques, such as grid search and random search, to optimize the hyperparameters of your model.
Use cross-validation: Use cross-validation techniques, such as k-fold cross-validation, to evaluate the performance of your model on unseen data.

By following these tips and tricks, you can improve the R² value and achieve high performance in your machine learning models.