# Minimum model

A project log for Discrete component object recognition

Inspired by a recent paper, the goal is to develop a system that can recognize numbers from the MNIST database without a microcontroller

# Goal

The goal of this task is to evaluate the best model for a discrete component object recognition system.

The first task would be to obtain a reference. Typical MNIST classifiers are well below 5% error rate (https://en.wikipedia.org/wiki/MNIST_database#Classifiers). We'll use a reference random forest, with no pre-processing on our reduced dataset.

# Benchmark model

The dataset was reduced using the tools shown in https://hackaday.io/project/170591/log/175172-minimum-sensor and evaluated using the same metrics from the previous task.

Training the Random Forest does tax the system quite significantly. The training of a 10k dataset using parallel processing consumed all 8 cores of my laptop at full blast for a good few minutes.

The resulting random forest has the following characteristics:

• 500 trees
• Accuracy 83.6%

Considering that:

• The pixel density used was 4 x 4, instead of 28x28; and,
• Only 10k records were used and there was no pre-processing on the data, the fit is OK.

The fit for individual values was good and most digits were over 80%.

```        0         1         2         3         4         5         6         7         8         9
0.8257426 0.8731284 0.9075908 0.8347743 0.8348018 0.8653367 0.9028459 0.8548549 0.7389061 0.7337058 ```

Our aim is to make a model, based on decision trees, that could be implemented using discrete components that should match the accuracy of the random forest model.

# Decision tree models

The choice of decision trees is due to the simplicity of this model with regards to its later implementation using discrete components.

Each split on a decision tree would take into account a single pixel and evaluated using discrete components.

By tuning the complexity parameters, we can increase the complexity of the tree in order to try and fit a decision tree that will approach the accuracy of the random forest.

The table below shows the effect on accuracy of the cp parameter:

```      cp      Accuracy
0.0010000000 0.6921987
0.0005000000 0.7162860
0.0003333333 0.7295383
0.0002500000 0.7375396
0.0002000000 0.7389565
0.0001666667 0.7412902
0.0001428571 0.7423737
0.0001250000 0.7422904
0.0001111111 0.7422904
0.0001000000 0.7411235```

The default cp parameter for the rpart package in R is 0.01 and with successive iterative reduction we obtain no visible increase in accuracy with cp below 0.00025 and we're still quite a way away from the target accuracy obtained with the random forest.

Even settling for a cp of 0.0025, assuming there's a limit on what can be achieved with decision trees, the result is mindboggling. The resulting tree has a total of 300 nodes or splits.

Could it be implemented using discrete components? Definitely. Maybe.

# Conclusion

Decision trees can achieve a reasonable accuracy at recognizing the MNIST dataset, at a complexity cost.

The resulting tree can reach a 73% accuracy which is just shy of the 75% target we set out in this project.