Export Confusion Matrix Python

Create data matrix from three columns in MS Excel. Download the file for your platform. How To Plot A Confusion Matrix In Python In this post I will demonstrate how to plot the Confusion Matrix. Amazon Simple Storage Service (S3) is an object storage service that offers high availability and reliability, easy scaling, security, and performance. Stack Exchange network consists of 175 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. 5 -> Forged) and calculate the Confusion Matrix and AUC curve. Building Random Forest Algorithm in Python In the Introductory article about random forest algorithm , we addressed how the random forest algorithm works with real life examples. 1-Regression-Trees) # - [8. PySpark shell with Apache Spark for various analysis tasks. A confusion matrix shows the number of true and false positives and negatives. @python_2_unicode_compatible class ConfusionMatrix (object): """ The confusion matrix between a list of reference values and a corresponding list of test values. mat file is also straight forward # write one variable x = np. This package will give you all types of measures such as confusion matrix, accuracy score, area under curve etc. A lasso regression analysis was conducted to identify a subset of predictors from a pool of 23 categorical and quantitative variables that best predicted a quantitative target variable. Discover how to prepare data. Let’s consider the following data. The output is a 6000 x 6000 matrix and I don't know how to make changes to the file such. 87, which means that the weight and the mpg move in exactly opposite directions roughly 87% of the time. It uses the Pandas function to_csv(). In the previous chapters of our Machine Learning tutorial (Neural Networks with Python and Numpy and Neural Networks from Scratch) we implemented various algorithms, but we didn't properly measure the quality of the output. If you are using only the Python interface, we recommend pickling the model object for best results. NumPy 2D array. More information can be found by searching for 'confusion matrix machine learning'. BTW in eCogntion also you can calculate confusion matrix. export_graphviz(). How to build a correlation matrix in Python Suppose you have an array of data, stocks returns for instance, and you are wondering whether there is some degree of correlation. The Maya Python API 2. Learn, teach, and study with Course Hero. pyplot as plt import os import json import itertools from sklearn. pandas, scikit-learn and xgboost integration. I'm looking for a Python library that can compute the confusion matrix for multi-label classification. 1 Regression Trees](#8. metrics import confusion_matrix from sklearn. TensorFlow Python reference documentation: gistfile1. In addition, corrplot is good at details, including choosing color, text labels, color labels, layout, etc. The loop way. You can vote up the examples you like or vote down the ones you don't like. Learn parameter tuning in gradient boosting algorithm using Python; Understand how to adjust bias-variance trade-off in machine learning for gradient boosting. Exporting ROC Curve and Confusion Matrix Learn more about machine learning, classification, roc, confusion matrix, svm, classification learner app, perfcurve, classperf Statistics and Machine Learning Toolbox. Best tool for printing tables in Jupyter notebook? Usage. The multi-class confusion matrix is a straightforward extension of the binary classifier two by two confusion matrix. The cells of the table are the number of predictions made by a machine learning algorithm. Random sampling with Python. How can I export my dataset with the predictive value in order to create a confusion matrix to determine the False Positive value? Thanks! python predictive-modeling training confusion-matrix. Turns out creating a virtual environment is pretty straightforward (as least in Anaconda) and super useful. Compute confusion matrix to evaluate the accuracy of a classification List of labels to index the matrix. tensorflow / tensorflow / python / ops / confusion_matrix. I found this diagram and explanation in Wikipedia very useful Source: Wikipedia “Consider a brain surgeon tasked with removing a cancerous tumor from a patient’s brain. ) In the BOTTOM RIGHT pane, find the tab "Python Console" and you should see "Terminal" is opened. Natural Language Processing with Python @ Udemy. balance()) Learning an artificial grammar using a reservoir The example code can be found at examples/grammar_task. The Pandas module is a high performance, highly efficient, and high level data analysis library. """ output = DataFrame output. This tip show how you can take a list of lists and flatten it in one line using list comprehension. Avkash Chauhan ([email protected] Rows in the confusion matrix. The following are code examples for showing how to use sklearn. Example 1: Creating a Scatter Plot Matrix. Original adaptation by J. When you need to illustrate your concepts to your clients, create a matrix with Microsoft Excel. A tree structure is constructed that breaks the dataset down into smaller subsets eventually resulting in a prediction. In this post you will discover how to save and load your machine learning model in Python using scikit-learn. Machine Learning with Python - The Basics. pyplot as plt import os import json import itertools from sklearn. You can base the model on any learner that is included in an R package in the Azure Machine Learning environment. I got smiles of imatinib as test molecule. Basically it is used for classification problem where the output can be of two or more types of classes. This is supported for Scala in Databricks Runtime 4. For this article, I will detail how to create a confusion matrix for a binary classification model both manually and using an sklearn built-in function called metrics. with scikit-learn models in Python. The H2O python module is not intended as a replacement for other popular machine learning frameworks such as scikit-learn, pylearn2, and their ilk, but is intended to bring H2O to a wider audience of data and machine learning devotees who work exclusively with Python. These classifiers can be combined in many ways to form different classification systems. For the predictive analytic, our main focus is the implementation of a logistic regression model a Decision tree and neural network. The DV is admission status (binary), and the IVs are: GRE score, GPA, and undergraduate prestige. max hit ratio k: (for multi-class only) Maximum number (top K) of predictions to use for hit ratio computation. 324-331 of "Introduction to Statistical Learning with Applications in R" by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. tree import export_graphviz import pydot # Pull out one tree from the forest tree = rf. { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "If you were not here for Lab 12, and need to install the graphviz package:" ] }, { "cell_type. The classifier can therefore get away with being "lazy" and picking the majority class unless it's absolutely certain that an example belongs to the other class. # -*- coding: utf-8 -*- # Load libraries import pandas from Python - exporting results to. Part of this Axes space will be taken and used to plot a colormap, unless cbar is False or a separate Axes is provided to cbar_ax. The cells of the table are the number of predictions made by a machine learning algorithm. You can vote up the examples you like or vote down the ones you don't like. I have tried the following naive code but it does not work, and I do not know how I can get one of the trees from a RandomForestClassifier:. An amazing result, considering we did practically no preprocessing on our data set before running it through AutoML. In Machine Learning, the types of Learning can broadly be classified into three types: 1. Rapid screening of microplast. There’s even a huge example plot gallery right on the matplotlib web site, so I’m not going to bother covering the basics here. Given an array or list of expected values and a list of predictions from your machine learning model, the confusion_matrix() function will calculate a confusion matrix and return the result as an array. There is some confusion amongst beginners about how exactly to do this. PythonでXgboost 2015-08-08. If you're not sure which to choose, learn more about installing packages. True - Export all the image chips, including those that do not overlap labeled data. tolist¶ method. confusion_matrix() will return a Python dictionary of responses, where each key is a dependent variable in the data set and each value is a Python dictionary representing the confusion matrix. max confusion matrix size: Maximum size (number of classes) for confusion matrices to print in the H2O logs. Confusion Matrix¶. A key takeaway from this discussion is that normally in your apps, you call APIs. pandas, scikit-learn, xgboost and seaborn integration - pandas-ml/pandas-ml. Let's See the confusion matrix visualization sns. It compares the observed and the predicted outcome values and shows the number of correct and incorrect predictions categorized by type of outcome. append(correl) #correlation is not a DataFrame. For example: A = [[1, 4, 5], [-5, 8, 9]] We can treat this list of a list as a matrix having 2 rows and 3 columns. The ConfusionMatrix visualizer is a ScoreVisualizer that takes a fitted scikit-learn classifier and a set of test X and y values and returns a report showing how each of the test values predicted classes compare to their actual classes. Learn parameter tuning in gradient boosting algorithm using Python; Understand how to adjust bias-variance trade-off in machine learning for gradient boosting. Download files. It takes a single function call in Matplotlib to generate a colorful confusion matrix plot. A key takeaway from this discussion is that normally in your apps, you call APIs. A lasso regression analysis was conducted to identify a subset of predictors from a pool of 23 categorical and quantitative variables that best predicted a quantitative target variable. In this exercise, you will dive more deeply into evaluating the performance of binary classifiers by computing a confusion matrix and generating a classification report. You may have noticed in the video that the classification report consisted of three rows, and an additional support column. The ConfusionMatrix class has a 'balance' (or 'normalise_per_class') method for this purpose. Part of this Axes space will be taken and used to plot a colormap, unless cbar is False or a separate Axes is provided to cbar_ax. We’ll be discussing it for classification, but it can certainly be used for regression. Sometimes, a data scientist is responsible for generating a confusion matrix using machine learning libraries like scikit-learn. tri(x, diag = FALSE) upper. For example, in our digits data set, there are ten classes for the digits, zero through nine. torch/models in case you go looking for it later. Using Pandas library. U_Accuracy is user accuracy, P_Accuracy is producer accuracy, and Kappa is the final computed percentage overall. Convert scikit-learn confusion matrix to pandas DataFrame - cm2df. When you need to illustrate your concepts to your clients, create a matrix with Microsoft Excel. Data items are converted to the nearest compatible builtin Python type, via the item function. This sample template will ensure your multi-rater feedback assessments deliver actionable, well-rounded feedback. xgboost package のR とpython の違い - puyokwの日記; puyokwさんの記事に触発されて,私もPythonでXgboost使う人のための導入記事的なものを書きます.ちなみに,xgboost のパラメータ - puyokwの日記にはだいぶお世話になりました.ありがとうございました.. Original adaptation by J. pandas_confusion. there are references for 2*2 matrix, but are not for. Decisions trees are the most powerful algorithms that. When a model is trained there is a way to investigate its performance and some specific metrics, such as the ROC curve and Confusion matrix. Applying models. confusion_matrix. Analysis of the Adult data set from UCI Machine Learning Repository¶. 2 Classification Trees](#8. evaluate import confusion_matrix fig, ax = plot_confusion_matrix(conf_mat=cm) plt. r2 stopping: Stop making trees when the R2 metric equals or exceeds this. This is the way we keep it in this chapter of our tutorial, but it can be the other way around as well, i. 324-331 of "Introduction to Statistical Learning with Applications in R" by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. You may have noticed in the video that the classification report consisted of three rows, and an additional support column. Turns out, doing this in Pandas is incredibly easy!. Confusion matrix from supervised learning. In the adjacency matrix , 1 denotes that a path exists between the two nodes while 0 denotes that no path exists between the two. Python Matrix. Decision Tree Classifier in Python using Scikit-learn. However, when I. And, I assure you that you will not have any confusion left after completing this article i. This allows more detailed analysis than mere proportion of correct classifications (accuracy). Recent Examples of matrix from the Web. My bad! It was a text mining competition. Ask Question Asked 6 years, but be able to add new rows/destinations and have the matrix update itself. Learn, teach, and study with Course Hero. But it's not uncommon to work with confusion matrices directly as pandas DataFrames. Principal component analysis in Matlab. metrics import classification_report, confusion_matrix, recall_score. In this video we see how to add information to a CSV file within Python. Decision Trees can be used as classifier or regression models. Using a database of breast cancer tumor information, you'll use a Naive Bayes (NB) classifer that predicts whether or not a tumor is malignant or benign. So now that you're somewhat comfortable with this, let's check out a popular use for correlation and covariance tables. The "-" sign can be used to remove columns/variables. If none is given, those that appear at least once in y_true or y_pred are used in sorted order. Use the ML. Using Pandas library. 3 toolset v140 and Windows 10 SDK (10. Default it 20. For more information on confusion matrices, please see mlxtend. A blog on data science in the world of software development. Use 0 to disable. py In this example, an Echo-State Network is trained to do next-word prediction on a set of sentences that. In the case of the ROC curve there is a way to export the underlying data – the “EXPORT DATA” button (see below). corr() correlation=correlation. Download files. If a decision flow contains a Python Code Node, the Python code will automatically be executed in the Python environment as part of the overall decision. If your python do not have pandas installed, do this in your terminal. If you want to pass in additional arguments, feel free to concatenate them to the "python_call" variable:. Entry *[r,t]* of this matrix is a count of the number of times that the reference value *r* corresponds to the test value *t*. Its focus is on supervised classification with several classifiers available: SVMs (based on libsvm), k-NN, random forests, decision trees. And, I assure you that you will not have any confusion left after completing this article i. Skip navigation Saving to CSV File in Python Matt Rutherford-West ROC Curve, Confusion Matrix, Accuracy Ratio. Use this data to evaluate your model's readiness. Function can be used to generate feature service that contains polygons on detected objects found in the imagery data using the designated deep learning model. What should you know? XGBoost (eXtreme Gradient Boosting) is an advanced implementation of gradient boosting algorithm. 47 KB from sklearn. Python package. ) or 0 (no, failure, etc. from sklearn. This package will give you all types of measures such as confusion matrix, accuracy score, area under curve etc. I need to take the data from a csv file, add it into arrays, and then create the confusion. , a deep learning model that can recognize if Santa Claus is in an image or not):. 3×3 Confusion Matrix; 8. Logistic Regression is a Machine Learning classification algorithm that is used to predict the probability of a categorical dependent variable. At the end of the PySpark tutorial, you will learn to use spark python together to perform basic data analysis operations. Original adaptation by J. This data set is meant for binary class classification - to predict whether the income of a person exceeds 50K per year based on some census data. In this post I will cover decision trees (for classification) in python, using scikit-learn and pandas. By default, all columns are considered. If I have a 10x15 matrix, with each row of the matrix corresponding to an observation, and each column corresponding to a variable, and I use that matrix as an input into the "pca" function, I see that the first two principal components explain ~90% of the variance in the data set by looking at the. corr() correlation=correlation. FYI: scikit-learn doesn't support multi-label for confusion matrix) What is the difference b. Python for Machine Learning and Data Mining 4. An Introduction to corrplot Package Introduction. Confusion matrix is an excellent method to illustrate the results of multi-class classification. They are extracted from open source Python projects. In this, we are mainly concentrating on the implementation of logistic regression in python, as the background concepts explained in how the logistic regression model works article. They are extracted from open source Python projects. What is Support Vector Machine? "Support Vector Machine" (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. Slides and Code Examples: bit. There are some numpy functions that contain underscores in the name (ie. Warmenhoven, updated by R. Download the file for your platform. ) In the BOTTOM RIGHT pane, find the tab "Python Console" and you should see "Terminal" is opened. It means random forest includes multiple decision trees which the average of the result of each decision tree would be the final outcome for random forest. Fragmenting "numerical Python" from "real Python" would be a major source of confusion. What (famous) games have been written in Python, with Pygame/Pyglet/Pyo Stack Exchange Network Stack Exchange network consists of 175 Q&A communities including Stack Overflow , the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. Here, I showed how to take a pre-trained PyTorch model (a weights object and network class object) and convert it to ONNX format (that contains the weights and net structure). Compute confusion matrix to evaluate the accuracy of a classification List of labels to index the matrix. Preamble: Twos-Complement Numbers. tree import DecisionTreeClassifier, export_graphviz. You can only go down or right. The table presents predictions on the x-axis and accuracy outcomes on the y-axis. It also performs feature selection. He is also an organizer for the Charlotte BI Group, a local PASS chapter in Charlotte, NC. This time, I’m going to focus on how you can make beautiful data visualizations in Python with matplotlib. Python In Greek mythology, Python is the name of a a huge serpent and sometimes a dragon. Here, I showed how to take a pre-trained PyTorch model (a weights object and network class object) and convert it to ONNX format (that contains the weights and net structure). Random Forest Introduction. unique(b_test, return_counts=True) b_test_pred = predict_whatever_your_model. py] # Compute the correlation matrix corr = d. k may be specified to be any positive integer less than the number of training cases, but is generally between 1 and 10. In this, we are mainly concentrating on the implementation of logistic regression in python, as the background concepts explained in how the logistic regression model works article. confusionMatrix" in tfjs. Building Random Forest Algorithm in Python In the Introductory article about random forest algorithm , we addressed how the random forest algorithm works with real life examples. WORK IN PROGRESS - Use it a your own risk. Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on "tidy" data. metrics import confusion_matrix from sklearn. xlrd module. After you create classification models interactively in Classification Learner, you can export your best model to the workspace. You can visualize the trained decision tree in python with the help of graphviz. Fragmenting "numerical Python" from "real Python" would be a major source of confusion. “hclust” for hierarchical clustering order is used in the following examples. corrcoef¶ numpy. Download files. This package will give you all types of measures such as confusion matrix, accuracy score, area under curve etc. A major motivation for this PEP is to reduce fragmentation. The "-" sign can be used to remove columns/variables. It is also possible to do it the other way around: enrich your C/C++ application by embedding Python in it. dot -o tree. All Python CS - Free download as PDF File (. A decision tree is a kind of machine learning algorithm that can be used for classification or regression. Example of confusion matrix usage to evaluate the quality of the output of a classifier on the iris data set. As continues to that, In this article we are going to build the random forest algorithm in python with the help of one of the best Python machine learning library. There are many websites out there that either are a paid service, or a heavily advertised that create a correlation matrix, and sometimes co-variance, matrix tables. 18) now has built in support for Neural Network models! In this article we will learn how Neural Networks work and how to implement them with the Python programming language and the latest version of SciKit-Learn!. Discover how to prepare. You can vote up the examples you like or vote down the ones you don't like. RForge strives to provide a colaborative environment for R package developers. A confusion matrix is basically a table with two dimensions namely "Actual" and "Predicted". Python Matrix. Model analysis. Compute confusion matrix to evaluate the accuracy of a classification List of labels to index the matrix. It's free to sign up and bid on jobs. Specify the labels as categorical vectors, or in one-of-N (one-hot) form. A blog on data science in the world of software development. He was appointed by Gaia (Mother Earth) to guard the oracle of Delphi, known as Pytho. So we'll do the "tf. You can also compute the confusion matrix on the training set. In this, we are mainly concentrating on the implementation of logistic regression in python, as the background concepts explained in how the logistic regression model works article. Import the Apyori library and import CSV data into the Model. By default, all columns are considered. pyplot as plt sns. Each map has excel sheet (not attribute. ai) VP, Enterprise Customers 2. model_selection import GridSearchCV, cross_validate, train_test_split, StratifiedKFold from. Download the file for your platform. In the last step we apply the network to the test data, convert the predicted probability into a class (p>0. Enter your email address to follow this blog and receive notifications of new posts by email. Principal component analysis in Matlab. Example of confusion matrix usage to evaluate the quality of the output of a classifier on the iris data set. This tip show how you can take a list of lists and flatten it in one line using list comprehension. Exporting ROC Curve and Confusion Matrix Learn more about machine learning, classification, roc, confusion matrix, svm, classification learner app, perfcurve, classperf Statistics and Machine Learning Toolbox. K-Means Clustering in Python with scikit-learn Learn about the inner workings of the K-Means clustering algorithm with an interesting case study. six import StringIO from IPython. Simple and efficient tools for data mining and data analysis; Accessible to everybody, and reusable in various contexts. And then we developed logistic regression using python on student dataset. Find how two nodes are related by showing all the paths in between them using Hubscope. Execute Python machine learning scripts in Azure Machine Learning Studio. Use this data to evaluate your model's readiness. By default, all columns are considered. In this post you will discover how to save and load your machine learning model in Python using scikit-learn. max hit ratio k: (for multi-class only) Maximum number (top K) of predictions to use for hit ratio computation. python Equal area sphere partitioning Wrapping C with Python: 3D image segmentation with region growing Compiling multiple modules with f2py Calling Fortran from Python with f2py A better confusion matrix with python The ROC curve Part 2 - Numerical Example with Python Machine Learning Part 2 - Numerical Example with. GSGSCMAT This example shows a scatter plot matrix with grouped data. An input array X is fed through two preprocessing pipelines and then to a set of base learners f(i). rows for predicted classes and columns for actual classes. Enter your email address to follow this blog and receive notifications of new posts by email. pyc files should not be checked into your source code repositories. Classification Decision trees from scratch with Python. References-Example 1 - Binary. Erfahren Sie mehr über die Kontakte von Vinay Kumar R und über Jobs bei ähnlichen Unternehmen. So we have the following three binary classification problems: {class1, class2}, {class1, class3}, {class2, class3}. api administration scenario notebook plugin export connection sql spark in a Custom Python Model. Learn, teach, and study with Course Hero. If you're not sure which to choose, learn more about installing packages. Search for jobs related to Python write matrix file or hire on the world's largest freelancing marketplace with 15m+ jobs. My bad! It was a text mining competition. I found this diagram and explanation in Wikipedia very useful Source: Wikipedia “Consider a brain surgeon tasked with removing a cancerous tumor from a patient’s brain. Python was created out of the slime and mud left after the great flood. Python Script widget can be used to run a python script in the input, when a suitable functionality is not implemented in an existing widget. Computer Vision is an AI based, that is, Artificial Intelligence based technology that allows computers to understand and label images. Entry *[r,t]* of this matrix is a count of the number of times that the reference value *r* corresponds to the test value *t*. Seaborn is a Python data visualization library based on matplotlib. # Making the Confusion Matrix. Label encodings (text labels to numeric labels) will be also lost. I am attempting to predict for a target variable using KNN and have done so with a K(17). Use matplotlib to display the confusion matrix as a graph. How To Plot A Confusion Matrix In Python In this post I will demonstrate how to plot the Confusion Matrix. Applying models. unique(b_test, return_counts=True) b_test_pred = predict_whatever_your_model. cor: a logical value indicating whether the calculation should use the correlation matrix or the covariance matrix. This article provides an extensive overview of tree-based ensemble models and the many applications of Python in machine learning. After a few hours of training, the script usually completes about 20,000 steps, printing out a final confusion matrix, and the accuracy percentage You can export to mobile devices in a compact form using:. confusion_matrix_one_vs_all (list of list) 2d list representing 2x2 one vs all matrix. If you set the optional keyword parameter split to True,. append(correl) #correlation is not a DataFrame. Entry *[r,t]* of this matrix is a count of the number of times that the reference value *r* corresponds to the test value *t*. In the NeuNetS tool, you can view or download performance metrics, including statistics about classes and a confusion matrix showing how well the model is performing. No matter how many disadvantages we have with logistic regression but still it is one of the best models for classification. ''' Online Python Compiler. The XGBoost python module is able to load data from: LibSVM text format file. This function generates a GraphViz representation of the decision tree, which is then written into out_file. Saving output of confusionMatrix as a. To switch the inline figure format to use SVG during an active session, do: To switch the inline figure format to use SVG during an active session, do:. Exporting ROC Curve and Confusion Matrix Learn more about machine learning, classification, roc, confusion matrix, svm, classification learner app, perfcurve, classperf Statistics and Machine Learning Toolbox. It has tons of classes and features which perform the complex mathematical analysis and give solutions in simple one or two lines of code so that we don't have to be a statistic genius or mathematical Nerd to learn data science and machine learning. 5% of the time your classifier is correct. Enter your email address to follow this blog and receive notifications of new posts by email. Warmenhoven, updated by R. The bags, made from nylon-fiber matrix out of a factory in Massachusetts, cover all fronts; from the all-day tote to the stylish crossbody, to the of-the-moment bucket bag, and are $55 to $325. Azure Machine Learning Workbench: Getting Started Today, we're going to take a look at one of the newest Data Science offerings from Microsoft. A confusion matrix shows the proper labels on the main diagonal (top left to bottom right). export_graphviz(). ) In the BOTTOM RIGHT pane, find the tab "Python Console" and you should see "Terminal" is opened. 3 × 1 export × 1. This competition went live for 103 days and ended on 20th December 2015. The old way would be to do this using a couple of loops one inside the other. xgboost package のR とpython の違い - puyokwの日記; puyokwさんの記事に触発されて,私もPythonでXgboost使う人のための導入記事的なものを書きます.ちなみに,xgboost のパラメータ - puyokwの日記にはだいぶお世話になりました.ありがとうございました.. Note, the pretrained model weights that comes with torchvision. The question being asked is, how does GRE score, GPA, and prestige of the undergraduate institution effect admission into graduate school. Predicting customer churn with Python: Logistic regression, decision trees and random forests Customer churn is when a company’s customers stop doing business with that company. Information about AI from the News, Publications, and ConferencesAutomatic Classification – Tagging and Summarization – Customizable Filtering and AnalysisIf you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the. dot -o tree. They are extracted from open source Python projects. CONFUSION_MATRIX does not support linear regression models. This blog post is part two in our three-part series of building a Not Santa deep learning classifier (i. 2-Classification-Trees.