ML Classification in Python | Monte Carlo CV | GBM Algo | IRIS | Data Science Tutorials | Pandas

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Machine learning is a method of teaching computers to learn from data, without being explicitly programmed. It is a powerful tool that can be used to analyze and understand complex datasets, make predictions and make informed decisions. In this article, we will be discussing how to use machine learning techniques to classify data using the IRIS dataset from UCI and the Gradient Boosting algorithm.

The IRIS dataset is a popular dataset used for machine learning classification tasks. It consists of 150 samples of iris flowers, each with four features: sepal length, sepal width, petal length, and petal width. The dataset also includes a label indicating the species of the iris. The goal of the classification task is to train a model that can accurately predict the species of a new iris flower based on its features.

The Gradient Boosting algorithm is a powerful machine learning technique that can be used for classification and regression tasks. It is an ensemble method that combines the predictions of multiple weak models to create a stronger model. The algorithm works by iteratively adding new models to the ensemble, where each model is trained to correct the errors made by the previous models.

One of the key benefits of using Gradient Boosting for classification tasks is that it can handle non-linear relationships between the features and the target variable. This makes it a great choice for datasets like the IRIS dataset, where the relationship between the features and the target variable is not always clear.

To implement the Gradient Boosting algorithm in Python, we will use the scikit-learn library, a popular machine learning library for Python. The first step is to import the required libraries and load the IRIS dataset. We will then split the dataset into training and test sets, so that we can evaluate the performance of the model on unseen data.

Next, we will define the Gradient Boosting model and set the hyperparameters. Hyperparameters are parameters that are not learned from the data, but are set by the user. In this case, we will set the number of trees, the learning rate and the maximum depth of the trees.

Once the model is defined and the hyperparameters are set, we can train the model on the training set and make predictions on the test set. To evaluate the performance of the model, we will use accuracy as the evaluation metric.

In addition to training the model, we can also use Monte Carlo Cross Validation (MCCV) to get a more robust estimate of the model’s performance. MCCV is a technique that involves training and evaluating the model multiple times with different random splits of the data. This allows us to get a more accurate estimate of the model’s performance and avoid overfitting.

To implement MCCV in Python, we can use the sklearn.model_selection.TimeSeriesSplit library, which allows us to easily split the data into train and test sets. We will then train the model and make predictions on the test set for each iteration, and calculate the mean accuracy.

In conclusion, Gradient Boosting is a powerful machine learning technique that can be used for classification tasks. By using the IRIS dataset from UCI and the scikit-learn library in Python, we were able to train a model that can accurately predict the species of a new iris flower based on its features. Additionally, by using Monte Carlo Cross Validation we were able to get a more robust estimate of the model’s performance. Machine learning is a very powerful tool that can be used to analyze and understand complex datasets, make predictions, and make informed decisions.

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: ML Classification in Python | Monte Carlo CV | GBM Algo | IRIS | Data Science Tutorials | Pandas.



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