classifier code

decision tree classifier python code example - dzone ai

Simply speaking, the decision tree algorithm breaks the data points into decision nodes resulting in a tree structure. The decision nodes represent the question based on which the data is split further into two or more child nodes. The tree is created until the data points at a specific child node is pure (all data belongs to one class). The criteria for creating the most optimal decision questions is the information gain. The diagram below represents a sample decision tree.

Decision trees build complex decision boundaries by dividing the feature space into rectangles. Here is a sample of how decision boundaries look like after model trained using a decision tree algorithm classifies the Sklearn IRISdata points. The feature space consists of two features namely petal length and petal width. The code sample is given later below.

Here is the code which can be used visualize the tree structure created as part of training the model. plot_treefunction from sklearn tree classis used to create the tree structure. Here is the code:

Here is how the tree would look after the tree is drawn using the above command. Note the usage of plt.subplots(figsize=(10, 10))for creating a larger diagram of the tree. Otherwise, the tree created is very small.

In the follow-up article, you will learn about how to draw nicer visualizations of a decision tree using package. Also, you will learn some key concepts in relation to decision tree classifiersuch as information gain (entropy, gini, etc).

bagging classifier python code example - data analytics

In this post, you will learn about the concept ofBaggingalong withBagging ClassifierPython code example. Bagging is also called bootstrap aggregation. It is a data sampling technique where data is sampled with replacement. Bagging classifier helps combine prediction of different estimators and in turn helps reduce variance.

Bagging classifier can be called as an ensemble meta-estimator which is created by fitting multiple versions of base estimator, trained with modified training data set created using bagging sampling technique (data sampled using replacement) or otherwise. The nagging sampling technique can result in the training set consisting of duplicate dataset or unique data set. This sampling technique is also called as bootstrap aggregation. The final predictor (also called as bagging classifier) combines the predictions made by each estimator / classifier by voting (classification) or by averaging (regression). Read more details about this technique in this paper, Bias, Variance and Arcing Classifiers by Leo Breiman. Another useful paper by Breiman is Bagging classifiers. While creating each of the individual estimators, one can configure the number of samples and/or features which need to be considered while fitting the individual estimators. Take a look at the diagram below to get a better understanding of the bagging classification.

Bagging classifier helps in reducing the variance of individual estimators by introducing randomisation into the training stage of each of the estimators and making an ensemble out of all the estimators. Note that the high variance means that changing the training data set results in the constructed or trained estimator by a great deal.

In this post, the bagging classifier is created using Sklearn BaggingClassifier with number of estimators set to 100, max_features set to 10, and max_samples set to 100 and the sampling technique used is default (bagging). The method applied is random patches as both the samples and features are drawn in the random manner.

Bagging classifier helps reduce the variance of unstable classifiers (having high variance). The unstable classifiers include classifiers trained using algorithms such as decision tree which is found to have high variance and low bias. Thus, one can get the most benefit of using bagging classifier for algorithms such as decision trees. The stable classifiers such as linear discriminant analysis which have low variance may not benefit much from bagging technique. You may want to check this post to get a better understanding of Bias and Variance concepts Bias & variance concepts and interview questions

In this section, you will learn about how to use Python Sklearn BaggingClassifier for fitting the model using Bagging algorithm. The following is done to illustrate how Bagging Classifier help improve the generalization performance of the model. In order to demonstrate the aspects of generalization performance, the following is done:

In this section, we will fit a bagging classifier using different hyperparameters such as the following and base estimator as pipeline built using Logistic Regression. Note that you can further performed a Grid Search or Randomized search to get the most appropriate estimator.

The model comes up with the following scores. Note that the model tends to overfit the data as the test score is 0.965 and training score is 0.974. However, the model will give better generalization performance than the model fit with Logistic Regression.

github - vmasrani/dementia_classifier: code for my masters thesis

Error: "RuntimeError: Python is not installed as a framework. The Mac OS X backend will not be able to function correctly if Python is not installed as a framework. See the Python documentation for more information on installing Python as a framework on Mac OS X. Please either reinstall Python as a framework, or try one of the other backends. If you are using (Ana)Conda please install python.app and replace the use of 'python' with 'pythonw'. See 'Working with Matplotlib on OSX' in the Matplotlib FAQ for more information. " See: https://stackoverflow.com/questions/21784641/installation-issue-with-matplotlib-python

github - codebox/bayesian-classifier: a naive bayesian classifier written in python

This is an implementation of a Naive Bayesian Classifier written in Python. The utility uses statistical methods to classify documents, based on the words that appear within them. A common application for this type of software is in email spam filters.

The utility must first be 'trained' using large numbers of pre-classified documents, during the training phase a database is populated with information about how often certain words appear in each type of document. Once training is complete, unclassified documents can be submitted to the classifier which will return a value between 0 and 1, indicating the probablity that the document belongs to one class of document rather than another.

how to build an image classifier in few lines of code with flash - kdnuggets

Image classification is a task where we want to predict which class belongs to an image. This task is difficult because of the image representation. If we flatten the image, it will create a long one-dimensional vector. Also, that representation will lose the neighbor information. Therefore, we need deep learning for extracting features and predict the result.

Sometimes, Building a deep learning model can become a difficult task. Although we create a base model for image classification, we need to spend lots of time creating the code. We have to prepare code for preparing the data, training the model, testing the model, and deploy it to the server. And thats where the Flash comes in!

Flash is a high-level deep learning framework for fast building, training, and testing the deep learning model. Flash is based on the PyTorch framework. So if you know PyTorch, you will be familiar with Flash easily.

In comparison with PyTorch and Lighting, Flash is easy to use but not so flexible as the previous libraries. If you want to build a more complex model, you can use Lightning or straight to the PyTorch.

With Flash, you can build your deep learning model in few lines of code! So, if you are new to deep learning, dont be afraid. Flash can help you to build a deep learning model without getting confused because of the code.

After we load the data, the next step is to load the model. Because we will not create our own architecture from scratch, we will use the pre-trained model based on existing convolutional neural network architecture.

After we load the model, now lets train the model. We need to initialize the Trainer object first. We will train the model in 3 epochs. Also, we enable the GPU to train the model. Here is the code for doing that:

After we initialize the object, now lets train the model. To train the model, we can use a function called finetune. Inside the function, we set the model and the data. Also, we set the training strategy to freeze, where we dont want to train the feature extractor. In other words, we train the classifier section only.

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how to build a machine learning classifier in python with scikit-learn | digitalocean

Machine learning is a research field in computer science, artificial intelligence, and statistics. The focus of machine learning is to train algorithms to learn patterns and make predictions from data. Machine learning is especially valuable because it lets us use computers to automate decision-making processes.

Youll find machine learning applications everywhere. Netflix and Amazon use machine learning to make new product recommendations. Banks use machine learning to detect fraudulent activity in credit card transactions, and healthcare companies are beginning to use machine learning to monitor, assess, and diagnose patients.

In this tutorial, youll implement a simple machine learning algorithm in Python using Scikit-learn, a machine learning tool for Python. Using a database of breast cancer tumor information, youll use a Naive Bayes (NB) classifer that predicts whether or not a tumor is malignant or benign.

The dataset we will be working with in this tutorial is the Breast Cancer Wisconsin Diagnostic Database. The dataset includes various information about breast cancer tumors, as well as classification labels of malignant or benign. The dataset has 569 instances, or data, on 569 tumors and includes information on 30 attributes, or features, such as the radius of the tumor, texture, smoothness, and area.

The data variable represents a Python object that works like a dictionary. The important dictionary keys to consider are the classification label names (target_names), the actual labels (target), the attribute/feature names (feature_names), and the attributes (data).

Attributes are a critical part of any classifier. Attributes capture important characteristics about the nature of the data. Given the label we are trying to predict (malignant versus benign tumor), possible useful attributes include the size, radius, and texture of the tumor.

We now have lists for each set of information. To get a better understanding of our dataset, lets take a look at our data by printing our class labels, the first data instances label, our feature names, and the feature values for the first data instance:

As the image shows, our class names are malignant and benign, which are then mapped to binary values of 0 and 1, where 0 represents malignant tumors and 1 represents benign tumors. Therefore, our first data instance is a malignant tumor whose mean radius is 1.79900000e+01.

You use the training set to train and evaluate the model during the development stage. You then use the trained model to make predictions on the unseen test set. This approach gives you a sense of the models performance and robustness.

The function randomly splits the data using the test_size parameter. In this example, we now have a test set (test) that represents 33% of the original dataset. The remaining data (train) then makes up the training data. We also have the respective labels for both the train/test variables, i.e. train_labels and test_labels.

There are many models for machine learning, and each model has its own strengths and weaknesses. In this tutorial, we will focus on a simple algorithm that usually performs well in binary classification tasks, namely Naive Bayes (NB).

After we train the model, we can then use the trained model to make predictions on our test set, which we do using the predict() function. The predict() function returns an array of predictions for each data instance in the test set. We can then print our predictions to get a sense of what the model determined.

Using the array of true class labels, we can evaluate the accuracy of our models predicted values by comparing the two arrays (test_labels vs. preds). We will use the sklearn function accuracy_score() to determine the accuracy of our machine learning classifier.

As you see in the output, the NB classifier is 94.15% accurate. This means that 94.15 percent of the time the classifier is able to make the correct prediction as to whether or not the tumor is malignant or benign. These results suggest that our feature set of 30 attributes are good indicators of tumor class.

You have successfully built your first machine learning classifier. Lets reorganize the code by placing all import statements at the top of the Notebook or script. The final version of the code should look like this:

Now you can continue to work with your code to see if you can make your classifier perform even better. You could experiment with different subsets of features or even try completely different algorithms. Check out Scikit-learns website for more machine learning ideas.

In this tutorial, you learned how to build a machine learning classifier in Python. Now you can load data, organize data, train, predict, and evaluate machine learning classifiers in Python using Scikit-learn. The steps in this tutorial should help you facilitate the process of working with your own data in Python.

decision tree classifier python code example - data analytics

Simply speaking, the decision tree algorithm breaks the data points into decision nodes resulting in a tree structure. The decision nodes represent the question based on which the data is split further into two or more child nodes. The tree is created until the data points at a specific child node is pure (all data belongs to one class). The criteria for creating the most optimal decision questions is the information gain. The diagram below represents a sample decision tree.

Decision treesbuild complex decision boundaries by dividing the feature space into rectangles. Here is a sample of how decision boundaries look like after model trained using a decision tree algorithm classifies the Sklearn IRIS data points. The feature space consists of two features namely petal length and petal width. The code sample is given later below.

Here is the code which can be used visualize the tree structure created as part of training the model. plot_tree function from sklearn tree class is used to create the tree structure. Here is the code:

Here is how the tree would look after the tree is drawn using the above command. Note the usage of plt.subplots(figsize=(10, 10)) for creating a larger diagram of the tree. Otherwise, the tree created is very small.

In the follow-up article, you will learn about how to draw nicer visualizations of decision tree using graphviz package. Also, you will learn some key concepts in relation to decision tree classifier such as information gain (entropy, gini etc).

random forest classifier python code example - data analytics

In this post, you will learn about how to train a Random Forest Classifier using Python Sklearn library. This code will be helpful if you are a beginner data scientist or just want to quickly get code sample to get started with training a machine learning model using Random Forest algorithm. The following topics will be covered:

Random forest can be considered as an ensemble of several decision trees. The idea is to aggregate the prediction outcome of multiple decision trees and create a final outcome based on averaging mechanism (majority voting). It helps the model trained using random forest to generalize better with larger population. In addition, the model becomes less susceptible to overfitting / high variance. Here are the key steps of random forest algorithm:

svm classifier using scikit learn - code examples - data analytics

In this section, you will see the usage of SGDClassifier (Note from sklearn.linear_model import SGDClassifier)which is native python implementation. The code below represents the implementation with default parameters.