I’m sure we’ve all seen this very annoying error before when using neural networks. I have personally spent weeks trying to debug my code in order to fix this error early in my Data Science Consultant career.

I want to make sure to address this error, how to understand the error and be prepared for when you see it again.

What does the error mean?

When you see the ValueError it usually means that your model (convolutional, lstm, etc.) ran into an input error. …

This will be a very short article about the papers published on this topic.

Summary

A research paper by the authors, Wei-Han. Lee and Ruby B. Lee, from one of Princeton Universities Department introduces the idea of always keeping your smartphone safe by using a multi-sensors-based system that continuously authenticates the user using your smartphone’s sensors.

The system is able to perform this task by learning the owner’s behavior patterns and environment characteristics, and then authenticates the current user without interrupting the user’s interaction. This helps combat smartphone thefts from impersonation attacks.

The system would use a sensors that best reflects the user’s…

This is part 4 out of 4 and in part 4, the results of all of the machine learning algorithms that were used will be looked over.

In other words this will be a very short blog…

Conclusion

• Majority of the models had problems classifying.
  
  - The metric that was used the most in order to make decisions with the data was F1 score.

Performances

Random Forest (Original)

Acc: 0.70

AUC ROC: 0.5473384593553532

TNR: 0.18208028387669106

FPR: 0.8179197161233089

FNR: 0.08740336516598454

TPR: 0.9125966348340154

Precision: 0.7312345139192538

Recall: 0.9125966348340154

F1 Score: 0.8119108306024194

Random Forest (Feature Selection/Importance > 0.05)

[‘Current Loan Amount’,
‘Credit Score’,
‘Annual Income’,
‘Monthly Debt’,
‘Number of Open Accounts’,
‘Current Credit Balance’,
‘Maximum Open Credit’,
‘Term_Short Term’,
‘Home Ownership_Home Mortgage’,
‘Home Ownership_Rent’]

Test Score: 0.6727296181630547

OOB Score: 0.7639080876521108

TNR: 0.3016189842537148

FPR: 0.6983810157462852

FNR: 0.17507958162801274

TPR: 0.8249204183719873

Precision: 0.7422866028316556

Recall: 0.8249204183719873 …

This blog is part 3 out of 4 and we will be discussing Boosting.

Gradient Boosting

This is part 2 out of 4 of the classification section.

K Nearest Neighbors

On the bank data we saw that our dependent variable is imbalanced, and on the pervious blog we discussed that the metric that we will be basing our results on was F1 Score using the Confusion Matrix. This blog will discuss, in depth, why.

Accuracy

Accuracy score is the most commonly used metric when it comes to performance measurement, but sometimes this can be a bad metric to base your results on.

Accuracy measures how many observations, both positive and negative, were correctly classified.

This can be misleading when having an imbalanced dataset, because if you have an imbalance dataset where the dependent variable is binary, there are 80% 1’s and 20% 0’s, then our model will develop an accuracy score where it calculates most of predicted variables as 1’s, maybe giving it a 90% accuracy score. …

Part 1 out of 4 will be short posts about the 4 different machine learning algorithms that were used on the bank data.

Random Forest

This will be a short post before we dive deep into classification in the next few blog posts.

If we look back on the banking data we will see that the dependent variable is heavily imbalanced. We can check the value counts by using the code below, and we can also get a visual representation using Seaborn’s count plot.

# Dependent variable is imbalanced
y_train.value_counts(normalize=True)sns.countplot(y_train)