XGBoost Random Forest Regression - XGBoost Random Forest Regressor

Edge Impulse Inc. / XGBoost Random Forest Regression Public

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Target: Cortex-M7 216MHz

[unpublished] XGBoost Random Forest Regressor settings

Training settings

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Split train/validation set on metadata key

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Input layer (8 features)

Edge Impulse Inc. / XGBoost Random Forest Regressor

Output layer (1 classes)

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Did you know? You have access to the full set of Keras layers through the Expert view (click on to switch),
or can even bring your own model (in PyTorch, Keras or scikit-learn).

Layer type
Dense Fully connected layer, the simplest form of a neural network layer. Use this for processed data, such as the output of a spectral analysis DSP block.
1D Convolution / pooling Learn features that take spatial information into account along a single dimension. Use this for raw data, or for DSP blocks that output spatial data, such as the MFCC block.
2D Convolution / pooling Learn features that take spatial information into account along two dimensions. Use this for raw data, or for DSP blocks that output spatial data, such as the MFCC block.
Reshape Turn one-dimensional data from a DSP block into multi-dimensional data. Use this as an input to a convolutional layer. Use this for deep learning on raw data, or to process MFCC output.
Flatten Flatten multi-dimensional data into a single dimension. You need to flatten data from a convolutional layer before returning.
Dropout Reduce the risk of a model overfitting your dataset by randomly cutting a fraction of network connections during training. Can be helpful if your model's training performance is better than its validation performance.

Complete architectures

Model	Author
Custom regression MLP Enterprise Example of a regression block	Edge Impulse Inc.
EfficientNetRegression Enterprise EfficientNetRegression	Edge Impulse Inc.
LGBM Random Forest Regressor Enterprise The LightGBM Random Forest Regressor applies an ensemble of decision trees with gradient boosting to provide fast, efficient, and accurate regression over large datasets.	Edge Impulse Inc.
XGBoost Random Forest Regressor Enterprise The XGBoost Random Forest Regressor combines the speed and performance of gradient boosting with the robustness of a random forest to achieve high-accuracy regression on complex datasets.	Edge Impulse Inc.
Random Forest Regressor Enterprise The scikit-learn Random Forest Regressor is an ensemble learning method that uses multiple decision trees to predict continuous values, offering high accuracy and handling complex datasets effectively.	Edge Impulse Inc.
Linear Regression Enterprise The scikit-learn Linear Regressor is a straightforward approach that models the relationship between a dependent variable and one or more independent variables using a linear equation to predict continuous outcomes.	Edge Impulse Inc.
Ridge Regression Enterprise The scikit-learn Ridge regressor applies L2 regularization to linear regression, balancing the fit and complexity of the model to improve prediction accuracy on new data by preventing overfitting.	Edge Impulse Inc.
RidgeCV Regression Enterprise The scikit-learn RidgeCV regressor combines ridge regression with built-in cross-validation to automatically tune the regularization strength, ensuring optimal balance between model complexity and performance on unseen data.	Edge Impulse Inc.

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	mathijs Edge Impulse staff

[unpublished] XGBoost Random Forest Regressor settings

Training settings

Augmentation settings

Advanced training settings

Neural network architecture

Model

Dense

1D Convolution / pooling

2D Convolution / pooling

Reshape

Flatten

Dropout

Custom regression MLP
Enterprise

EfficientNetRegression
Enterprise

LGBM Random Forest Regressor
Enterprise

XGBoost Random Forest Regressor
Enterprise

Random Forest Regressor
Enterprise

Linear Regression
Enterprise

Ridge Regression
Enterprise

RidgeCV Regression
Enterprise

Description

Last training performance (validation set)

Accuracy

Loss

Confusion matrix (validation set)

Feature explorer (full training set)

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[unpublished] XGBoost Random Forest Regressor settings

Training settings

Augmentation settings

Advanced training settings

Neural network architecture

Model

Description

Last training performance (validation set)

Accuracy

Loss

Confusion matrix (validation set)

Feature explorer (full training set)

Settings