Manual hyperparameter tuning. Built-in Tunable Models Jan 16, 2023 · Hyperparameter tuning is important because the performance of a machine learning model is heavily influenced by the choice of hyperparameters. Feb 4, 2016 · In this post you will discover three ways that you can tune the parameters of a machine learning algorithm in R. Start hyperparameter tuning trials by executing in terminal: ray submit cluster_config_cpu. Azure Machine Learning lets you automate hyperparameter tuning Nov 7, 2020 · As can be seen in the above figure [1], the hyperparameter tuner is external to the model and the tuning is done before model training. Feb 5, 2024 · Optuna is an open-source hyperparameter optimization framework designed for automating the process of tuning machine learning model hyperparameters. May 17, 2021 · In this tutorial, you will learn how to tune model hyperparameters using scikit-learn and Python. 1. Hyperparameter tuning is an optimization technique and is an essential aspect of the machine learning process. The design of an HPO algorithm depends on the nature of the task and its context, such as the optimization budget and available information. In this guide, we will keep working on the forged bank notes use case, understand what SVM parameters are already being set by Scikit-Learn, what are C and Gamma hyperparameters, and how to tune them using cross validation and grid search. learning rate), and (3) the number of training steps. Sep 22, 2019 · For what I know, and correct me if I am wrong, the use of cross-validation for hyperparameter tuning is not advisable when I have a huge dataset. Download and prepare the dataset. Choosing the right set of hyperparameters can lead to Nov 6, 2020 · As such, it offers an efficient alternative to less efficient hyperparameter optimization procedures such as grid search and random search. On the contrary, hyperparameters are the parameters of a neural network that is fixed by design and not tuned by training. In this article, I will demonstrate the process to tune 2 things of Neural Network: (1) the hyperparameters and (2) the layers. Mar 31, 2020 · Want to learn more? Take the full course at https://learn. This process is an essential part of machine learning, and choosing appropriate hyperparameter values is crucial for success. Note. A hyperparameter is a model argument whose value is set before the learning process begins. depth, min_child_weight, subsample, colsample_bytree, gamma. Apr 21, 2023 · Customization: Hyperparameter tuning allows for customization of the model to fit specific requirements, such as specific object detection tasks. Pick from popular search methods such as Bayesian, grid search, and random to search the hyperparameter space. Running KerasTuner with TensorBoard will give you additional features for visualizing hyperparameter tuning results using its HParams plugin. While the hyperparameter tuning process is ongoing, you will see the status updates in terminal such as the screenshot Jul 2, 2023 · This guide is the second part of three guides about Support Vector Machines (SVMs). Sep 4, 2015 · In this example I am tuning max. Figure 4-1. Tune hyperparameters in your custom training loop. Walk through a real example step-by-step with working code in R. In the Transformers 3. Mar 26, 2024 · Step 6: Tuning Hyperparamers and fitting the model to the training data. See full list on geeksforgeeks. In a nutshell — you want a model with more than 97% accuracy on the test set. Hyperparameter types: K in K-NN; Regularization constant, kernel type, and constants in SVMs Aug 27, 2021 · Hypertuning is an essential part of a machine learning pipeline. References. GridSearch, Bayesian optimization, Hyperopt, and other methods are popular Jul 3, 2024 · Hyper parameter tuning are supplied as arguments to the model algorithm during initializing them as key, value and their values are picked by the data scientist, who is building the model in iterative mode. Use the code as a template to tune machine learning algorithms on your current or next machine learning project. Gini index – Gini impurity or Gini index is the measure that parts the probability Sep 13, 2023 · Hyperparameter Tuning Strategies. It is a deep learning neural networks API for Python. Grid Search is exhaustive and Random Search, is well… random, so could miss the most important values. While this is an important step in modeling, it is by no means the only way to improve performance. This is also called tuning . May 3, 2023 · Hyperparameter tuning is a crucial step in machine learning that can significantly improve the performance of a model. Hyperparameter optimization finds a tuple of hyperparameters that yields an optimal Dec 21, 2021 · In lines 1 and 2, we import GridSearchCV from sklearn. Oct 28, 2019 · Non-trainable params: 0 (0. An optimization procedure involves defining a search space. Search space is the range of value that the sampler should consider from a hyperparameter. There are several strategies for hyperparameter tuning, but we will focus on two popular methods: Grid Search and Random Search. I find it more difficult to find the latter tutorials than the former. However, another way to save time when performing hyperparameter tuning on large data sets is to pre-augment your data set instead of using on the fly augmentation. You will use the Pima Indian diabetes dataset. Grid Search. Hyperopt has four important features you Jul 7, 2021 · Hyperparameter tuning is a vital aspect of increasing model performance. It is the key to unlocking the full potential of your models, ensuring they perform well on unseen data and in Tuning in tidymodels requires a resampled object created with the rsample package. Our first choice of hyperparameter values, however, may not yield the best results. Hyperparameter tuning, also called hyperparameter optimization, is the process of finding the configuration of hyperparameters that results in the best performance. Apr 9, 2022 · Hyperparameter Tuning. Therefore, the method you choose to carry out hyperparameter tuning is of high importance. datacamp. Scikit-learn provides these two methods for algorithm parameter tuning and examples of each are provided below. Running the example shows the same general trend in performance as a batch size of 4, perhaps with a higher RMSE on the final epoch. It can optimize a model with hundreds of parameters on a large scale. Let’s see if hyperparameter tuning can do that. . The Keras Tuner package makes it dead simple to tune your model hyperparameters by: Requiring just a single import. Given a complex model with many hyperparameters, effective hyperparameter tuning may drastically improve performance. Sometimes it chooses a combination of hyperparameter values close to the combination that resulted in the A hyperparameter tuning job uses the objective metric that each training job returns to evaluate training jobs. Two simple and easy search strategies are grid search and random search. Scale and parallelize sweep across one or more machines. Before starting the tuning process, we must define an objective function for hyperparameter optimization. Tuning may be done for individual Estimator s such as LogisticRegression, or for entire Pipeline s which include multiple algorithms, featurization, and Oct 30, 2020 · Here’s how we can speed up hyperparameter tuning with 1) Bayesian optimization with Hyperopt and Optuna, running on… 2) the Ray distributed machine learning framework, with a unified Ray Tune API to many hyperparameter search algos and early stopping schedulers, and… 3) a distributed cluster of cloud instances for even faster tuning. Grid search is a hyperparameter tuning technique that performs an exhaustive search over a specified hyperparameter space to find the combination of hyperparameters that yields the best model performance. This is the fourth article in my series on fully connected (vanilla) neural networks. Feb 28, 2017 · To clarify the -> Perform hyperparameter tuning step, you can read about the recommended approach of nested cross validation. Searching for optimal parameters with successive halving# Apr 11, 2017 · In this section, we look at halving the batch size from 4 to 2. Tips & Tricks The key takeaway here is that Population Based Training is the most effective approach to tune the hyperparameters of the Model selection (a. cv in that function with the hyper parameters set to in the input parameters of xgb. Now note that, as expected, isolated tuning turned out to be a pretty fast algorithm. As Figure 4-1 shows, each trial of a particular hyperparameter setting involves training a model—an inner optimization process. While the hyperparameter tuning job is in progress, the best training job is the one that has returned the best objective metric so far. It provides a flexible and efficient platform Hyperparameter tuning allows data scientists to tweak model performance for optimal results. It should be noted that some of the code shown below were adapted from scikit-learn. Ray Tune includes the latest hyperparameter search algorithms, integrates with TensorBoard and other analysis libraries, and natively supports distributed training through Ray’s distributed machine learning engine . The criteria support two types such as gini (Gini impurity) and entropy (information gain). This object is passed to the objective function to specify which hyperparameters should be tuned. Jun 7, 2021 · 5. The dataset corresponds to a classification problem on which you need to make predictions on the basis of whether a person is to suffer diabetes given the 8 features in the dataset. Hyperparameter optimization (HPO) is a method that helps solve the challenge of tuning hyperparameters of machine learning models. We are ready to tune! Let’s use tune_grid() to fit models at all the different values we chose for each tuned hyperparameter. sweep() method. The guide is mostly going to focus on Lasso examples, but the Tune Hyperparameters. Jan 9, 2018 · Hyperparameter tuning relies more on experimental results than theory, and thus the best method to determine the optimal settings is to try many different combinations evaluate the performance of each model. number of layers), (2) the optimizer hyperparameters (e. Jan 27, 2021 · Hyperparameter tuning is an important part of developing a machine learning model. But it’ll be a tedious process. Jun 13, 2024 · Hyperparameter-tuning is important to find the possible best sets of hyperparameters to build the model from a specific dataset. We had to choose a number of hyperparameters for defining and training the model. However, evaluating each model only on the training set can lead to one of the most fundamental problems in machine learning: overfitting. In line 3, the hyperparameter values are defined as a dictionary where keys are the hyperparameter name and a list of values containing hyperparameter values we want to try. Consider the following setup: StratifiedKFold, cross_val_score. As an example, let’s say we want to tune three hyperparameters: the learning rate, the number of units of a layer, and the optimizer of our neural network model. We’ll start the tutorial by discussing what hyperparameter tuning is and why it’s so important. Set use_predefined_hps=True to automatically configure the search space for the hyper-parameters. This change is made to the n_batch parameter in the run () function; for example: n_batch = 2. Define a search space as a bounded domain of hyperparameter values and randomly sample points in that domain. Getting started with KerasTuner. Kick-start your project with my new book Machine Jul 13, 2024 · import keras_tuner as kt. 2. Note: The automatic hyper-parameter configuration explores some powerful but slow to train hyper-parameters. com/courses/hyperparameter-tuning-in-r at your own pace. The scikit-optimize library can be installed using pip, as follows: sudo pip install scikit-optimize. 5. n_batch=2. Most importantly, we will do this in a similar way to how top Kagglers tune their LGBM models that achieve impressive results. We are going to use Tensorflow Keras to model the housing price. By specifying a parameter distribution containing ranges or distributions for hyperparameters such as the number of estimators Aug 28, 2021 · The basic way to perform hyperparameter tuning is to try all the possible combinations of parameters. Hyperparameter Tuning. Scikit-Learn provides powerful tools like RandomizedSearchCV and GridSearchCV to help you Before beginning hyperparameter tuning we must determine the starting point. The best performing HPC λ ̂ returned by tuning is then used to fit a final model for the current outer loop on the outer training set, and this model is then cleanly evaluated on the test set. May 16, 2021 · 1. Jan 29, 2020 · You can also view the optimal hyperparameter values found by the search: best_hyperparameters = tuner. May 31, 2019 · KerasTuner is a general-purpose hyperparameter tuning library. Choosing the right set of hyperparameters can be the difference between an average model and a highly accurate one. Load the data. Let me now introduce Optuna, an optimization library in Python that can be employed for Jul 9, 2019 · Image courtesy of FT. Random Search. This chapter covers techniques to detect outliers in 1-dimensional data using histograms, scatterplots, box plots, z-scores, and modified z-scores. 5%. There are 3 ways in scikit-learn to find the best C by cross validation. Finally, we hypertuned a predefined HyperResnet model. For example, assume you're using the learning rate of the model as a hyperparameter. Ray Tune is an industry-standard tool for distributed hyperparameter tuning that integrates seamlessly Apr 21, 2023 · Optuna is a hyperparameter tuning library that is specifically designed to be framework agnostic. hyperparameter tuning) An important task in ML is model selection, or using data to find the best model or parameters for a given task. , Random search for hyper-parameter optimization, The Journal of Machine Learning Research (2012) 3. By contrast, the values of other parameters such as coefficients of a linear model are learned. g. Interpreting a decision tree should be fairly easy if you have the domain knowledge on the dataset you are working with because a leaf node will have 0 gini index because it is pure, meaning all the samples belong to one class. Bergstra, J. For example, if you want to tune the learning_rate and the max_depth, you need to specify all the values you think will be relevant for the search. Tune is a Python library for experiment execution and hyperparameter tuning at any scale. Feb 29, 2024 · Hyperparameter Tuning using Randomized Search CV. This can be thought of geometrically as an n-dimensional volume, where each hyperparameter represents a different dimension and the scale of the dimension are the values that the hyperparameter Apr 8, 2023 · The “weights” of a neural network is referred as “parameters” in PyTorch code and it is fine-tuned by optimizer during training. flow () Source: Keras Docs. In this comprehensive guide, we will cover the key hyperparameters to tune in LightGBM, various hyperparameter tuning approaches and tools, evaluation metrics to use, and walk through a case study demonstrating the hyperparameter tuning process on a sample dataset. 00 B) In summary, to tune the hyperparameters in your custom training loop, you just override HyperModel. 1 release, Hugging Face Transformers and Ray Tune teamed up to provide a simple yet powerful integration. Now we will be performing the tuning of hyperparameters of the random forest model. %tensorboard --logdir logs/hparam_tuning. This returns the Sep 18, 2020 · This is called hyperparameter optimization, hyperparameter tuning, or hyperparameter search. 45. Ray Sep 4, 2023 · Conclusion. Dec 30, 2022 · Hyperparameter tuning is a crucial step in the machine learning pipeline that can significantly impact the performance of a model. Bayesian Optimization can be performed in Python using the Hyperopt library. org Ray Tune is an industry standard tool for distributed hyperparameter tuning. In this post, we are first going to have a look at some common mistakes when it comes to Lasso and Ridge regressions, and then I’ll describe the steps I usually take to tune the hyperparameters. It is highly important to select the hyperparameters of DBSCAN algorithm rightly for your dataset and the domain in which it belongs. We’ll then have three Python scripts to implement: Tune hyperparameters. 1. Apr 20, 2020 · Notice that the objective function is passed an Optuna specific argument of trial. Distributed hyperparameter tuning with KerasTuner. A hyperparameter is a parameter whose value is used to control the learning process. The key to machine learning algorithms is hyperparameter tuning. It can monitor the losses and metrics during the model training and visualize the model architectures. We explored Keras Tuner in-depth and how it is used to automate the hyperparameter search. Hyperparameter sweeps provide an organized and efficient way to conduct a battle royale of models and pick the most accurate model. If you’re leveraging Transformers, you’ll want to have a way to easily access powerful hyperparameter tuning solutions without giving up the customizability of the Transformers framework. Jun 28, 2022 · Figure 6. Vertex AI keeps track of the results of each trial and makes adjustments for subsequent trials. You can tune your favorite machine learning framework ( PyTorch, XGBoost, TensorFlow and Keras, and more) by running state of the art algorithms such as Population Based Training (PBT) and HyperBand/ASHA . Use W&B Sweeps to automate hyperparameter search and visualize rich, interactive experiment tracking. cv. Improve your model accuracy with practical tips and tricks. Aug 9, 2017 · Learn what hyperparameters are and how to tune them in a deep neural network. The HParams dashboard can now be opened. Jan 6, 2022 · Visualize the results in TensorBoard's HParams plugin. The code is in Python, and we are mostly relying on scikit-learn. Jun 12, 2024 · Here, we explored three methods for hyperparameter tuning. bayes . Mar 15, 2020 · Step #2: Defining the Objective for Optimization. You then call xgb. sudo pip install scikit-optimize. How Grid Search Works . Visualize the hyperparameter tuning process. eps hyperparameter. In machine learning, hyperparameter optimization [1] or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. In this article, we will be optimizing a neural network and performing hyperparameter tuning in order to obtain a high-performing model on the Beale function — one of many test functions commonly used for studying the effectiveness of various optimization techniques. and Bengio, Y. Then, when we run the hyperparameter tuning, we try all the combinations from both lists. In this tutorial, you will see how to tune model architecture, training process, and data preprocessing steps with KerasTuner. The algorithm predicts based on the keyword in the dataset. Step 7: Evaluate the model performance score and assess the final hyperparameters. The 2 hyperparameters that we will tune includes max_features and the n_estimators. This is repeated for all outer loops, and all outer Hyperparameter Optimization in AutoMM. Hyperparameter tuning works by running multiple trials of your training application with values for your chosen hyperparameters, set within limits you specify. We relied on intuition, examples and best practice recommendations. With the provided callbacks, you can easily save the trained models at their best epochs and load the best models later. yml tune_cifar10. So, in this case it is better to split the data in training, validation and test set; and then perform the hyperparameter tuning with the validation set. In this article, I illustrate the importance of hyperparameter tuning by comparing the predictive power of logistic regression models with various hyperparameter values. Sep 26, 2019 · Automated Hyperparameter Tuning. Examples are the number of hidden layers and the choice of activation functions. A hyperparameter is a model argument whose value is set before the le arning process begins. We will use a simple Oct 12, 2020 · Hyperopt. Optuna offers three distinct features that make it an optimal hyperparameter optimization framework: Eager search spaces: automated search for optimal hyperparameters Comparing randomized search and grid search for hyperparameter estimation compares the usage and efficiency of randomized search and grid search. Jul 9, 2024 · Hyperparameter tuning overview. ML algorithms have multiple complex hyperparameters that generate an enormous search space, and the search space in deep learning methods is even larger than traditional ML As before, hyper-parameter tuning is enabled by specifying the tuner constructor argument of the model. py # To trial run scripts, add argument smoke-test # ray submit cluster_config_cpu. Nov 5, 2021 · Hyperparameter tuning is an essential part of the Data Science and Machine Learning workflow as it squeezes the best performance your model has to offer. Feb 20, 2020 · 5. When the job is finished, you can get a summary of all Sep 3, 2021 · Then, we will see a hands-on example of tuning LGBM parameters using Optuna — the next-generation bayesian hyperparameter tuning framework. A range of different optimization algorithms may be used, although two of the simplest and most common methods are random search and grid search. com. Jul 9, 2024 · How hyperparameter tuning works. k. It involves defining a grid of hyperparameters and evaluating each one. Tailor the search space. In machine learning, hyperparameter tuning identifies a set of optimal hyperparameters for a learning algorithm. 3. Nov 13, 2019 · What is hyperparameter tuning ? Hyper parameters are [ SVC(gamma=”scale”) ] the things in brackets when we are defining a classifier or a regressor or any algo. Before starting, you’ll need to know which hyperparameters you can tune. . There are several options for building the object for tuning: Tune a model specification along with a recipe Hyperparameter tuning is a meta-optimization task. Keras documentation. Hyperparameter tuning uses an Amazon SageMaker implementation of Bayesian optimization. Jan 27, 2021 · Suppose we are predicting if a newly arrived email is spam or not. If you enjoyed this explanation about hyperparameter tuning and wish to learn more such concepts, join Great Learning Academy’s free courses today. The process is typically computationally expensive and manual. # define a pipeline @pipeline() def pipeline_with_hyperparameter_sweep(): """Tune hyperparameters using sample components. I highly suggest reading the first part of the article if you are new to LGBM. Hyperparameter tuning is a crucial step in building machine-learning models that perform well. load_data () Nov 2, 2020 · 70. The outcome of hyperparameter tuning is the best hyperparameter setting, and the outcome of model training is the best model parameter setting. This means that you can use it with any machine learning or deep learning framework. Oct 16, 2023 · Hyperparameter tuning is an indispensable part of machine learning model development. The left pane of the dashboard provides filtering capabilities that are active across all the views in the HParams dashboard: Jun 1, 2020 · Using ImageDataGenerator with datagen. Grid Jul 3, 2018 · Hyperparameter setting maximizes the performance of the model on a validation set. Code for 30 repetitions / average statistics of the 30 repetitions. Execution times of the hyperparameter tuning module runs for some pipelines when solving a regression problem (image by author) Let’s start with the obvious: the more iterations allocated, the longer the algorithm runs. The result of the tuning process is the optimal values of hyperparameters which is then fed to the model training stage. It has strong integration with Keras workflows, but it isn't limited to them: you could use it to tune scikit-learn models, or anything else. ¶. We define the hyperparameter search space as a parameter grid. Machine learning algorithms frequently require to fine-tuning of model hyperparameters. When using Automated Hyperparameter Tuning, the model hyperparameters to use are identified using techniques such as: Bayesian Optimization, Gradient Descent and Evolutionary Algorithms. Try in a Colab Notebook here →. (img_train, label_train), (img_test, label_test) = keras. It uses a form of Bayesian optimization for parameter tuning that allows you to get the best parameters for a given model. Tune Model Hyperparameters can only be connect to built-in machine learning algorithm components, and cannot support customized model built in Create Python Model. It only gives us a good starting point for training. Jun 7, 2021 · In this tutorial, you learned how to easily tune your neural network hyperparameters using Keras Tuner and TensorFlow. 48 min. Hyperparameter Optimization (HPO) algorithms aim to alleviate this task as much as possible for the human expert. Unfortunately, that tuning is often called as ‘ black function ’ because it cannot be written into a formula since the derivates of the function are unknown. py --smoke-test. Grid search is a traditional method of performing hyperparameter tuning. In this post, we trained a baseline model showing why manual searching for optimal hyperparameters is hard. datasets. Apr 16, 2024 · The major hyperparameters that are used to fine-tune the decision: Criteria : The quality of the split in the decision tree is measured by the function called criteria. fit() to train the model and return the evaluation results. get_best_hyperparameters(1)[0] And that’s all the code that is needed to perform a sophisticated hyperparameter search! You can find the complete code for the example above here. Oct 12, 2021 · This is called hyperparameter optimization, or hyperparameter tuning. Image by author. Model tuning with a grid. Dec 14, 2021 · In every hyperparameter tuning session, we need to define a search space for the sampler. […] Hyperparameter tuning is a final step in the process of applied machine learning before presenting results. Searching through high dimensional hyperparameter spaces to find the most performant model can get unwieldy very fast. Sep 30, 2023 · Tuning these hyperparameters is essential for building high-quality LightGBM models. When choosing the best hyperparameters for the next training job, hyperparameter tuning considers everything that it knows about this problem so far. In this tutorial, you will use the Keras Tuner to find the best hyperparameters for a machine learning model that classifies images of clothing from the Fashion MNIST dataset. From there, we’ll configure your development environment and review the project directory structure. Thus, I repeated, and Here is an example of Hyperparameter tuning of Isolation Forest: . Bayesian Optimization. Mar 25, 2022 · Feb 25, 2013 at 10:10. The Code. model_selection and define the model we want to perform hyperparameter tuning on. If you augment your data during the process of building your binaries, you prevent the need to dedicate CPU/GPU Ray Tune is an industry standard tool for distributed hyperparameter tuning. Handling failed trials in KerasTuner. Hyperopt is a powerful Python library for hyperparameter optimization developed by James Bergstra. Allowing you to define the values and ranges inside your model architecture. a. Feb 23, 2023 · In Azure Machine Learning Python SDK v2, you can enable hyperparameter tuning for any command component by calling . Cons of hyper-parameter tuning: Time-consuming: Hyperparameter tuning can be time-consuming and requires a significant amount of experimentation to determine the optimal hyperparameters. Jun 5, 2021 · TensorBoard is a useful tool for visualizing the machine learning experiments. More than a video, you'll learn Apr 12, 2021 · Hyperparameter tuning is choosing a set of optimal hyperparameters for a learning algorithm. This includes specifying (1) the model configuration (e. $2. In penalized logistic regression, we need to set the parameter C which controls regularization. Add the dataset that you want to use for training, and connect it to the middle input of Tune Model Hyperparameters. Below code snippet shows how to enable sweep for train_model. bookmark_border. Step 8: If the model performance is Aug 21, 2019 · Phrased as a search problem, you can use different search strategies to find a good and robust parameter or set of parameters for an algorithm on a given problem. Below are some of the different flavors of performing HPO. For example, a gradient boosting classifier has many different parameters to fine-tune, each uniquely changing the model’s performance. fashion_mnist. Tune further integrates with a wide range of Jul 18, 2022 · Step 5: Tune Hyperparameters. Hyperparameter optimization. A good choice of hyperparameters may make your model meet your Jan 16, 2023 · Each proposed HPC λ + during tuning is evaluated via inner resampling on the outer training set. In order to determine the best value of eps for your dataset, use the K-Nearest Neighbours approach as explained in these two papers: Sander et al. This post is about the differences between LogisticRegressionCV, GridSearchCV and cross_val_score. 1998 and Schubert et Aug 25, 2020 · Comparison of 3 different hyperparameter tuning approaches. You don’t need a dedicated library for hyperparameter tuning. Available guides. Nov 11, 2019 · The best way to tune this is to plot the decision tree and look into the gini index. Nov 12, 2021 · One of the solutions is to repeat the prediction several times and calculate statistics of those results. Start TensorBoard and click on "HParams" at the top. Jun 25, 2024 · Model performance depends heavily on hyperparameters. """. This code snippet demonstrates the utilization of RandomizedSearchCV to perform hyperparameter tuning for the Gradient Boosting Classifier on the Titanic dataset. Nov 14, 2021 · Connect an untrained model to the leftmost input. The idea is to test the robustness of a training process by repeatedly performing the training and testing process on different folds of the data, and looking at the average of test results. While analyzing the new keyword “money” for which there is no tuple in the dataset, in this scenario, the posterior probability will be zero and the model will assign 0 (Zero) probability because the occurrence of a particular keyword class is zero.
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