Random forest supervised or unsupervised11/25/2023 ![]() ![]() If set to False, no intercept will be used in calculations: model = LinearRegression () param_grid = For linear regression, the hyperparameter is fit_intercept, which is a boolean variable that determines whether or not to calculate the intercept for this model. We then define a dictionary where the keywords name the hyperparameters and the values list the parameter settings to be The first step is to create a model object. The GridSearchCV class in the model_selection module of the sklearn package facilitates the systematic evaluation of all combinations of the hyperparameter values that we would like to test. The drawback is that the size of the grid grows exponentially with the addition of more parameters or more considered values. These hyperparameters are tuned during grid search to achieve better model performance.ĭue to its exhaustive search, a grid search is guaranteed to find the optimal parameter within the grid. Hyperparameters are the external characteristic of the model, can be considered the model’s settings, and are not estimated based on data-like model parameters. The overall idea of the grid search is to create a grid of all possible hyperparameter combinations and train the model using each one of them. For a thorough coverage of the topics, the reader is referred to Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition, by Aurélien Géron (O’Reilly). In this chapter, we present a high-level overview of supervised learning models. With easy-to-use machine learning libraries like Scikit-learn and Keras, it is straightforward to fit different machine learning models on a given predictive modeling dataset. Some of these libraries were covered in Chapter 2. Python and its libraries provide methods and ways to implement these supervised learning models in few lines of code. Many use cases of regression-based and classification-based supervised machine learning are presented in Chapters 5 and 6. There are many other use cases of classification-based supervised learning in portfolio management and algorithmic trading. These include fraud detection, default prediction, credit scoring, directional forecast of asset price movement, and Buy/Sell recommendations. There are many other use cases of regression-based supervised learning in portfolio management and derivatives pricing.Ĭlassification-based algorithms, on the other hand, have been leveraged across many areas within finance that require predicting a categorical response. These models are used to predict returns over various time periods and to identify significant factors that drive asset returns. Regression-based algorithms have been leveraged by academic and industry researchers to develop numerous asset pricing models. Many algorithms that are widely applied in algorithmic trading rely on supervised learning models because they can be efficiently trained, they are relatively robust to noisy financial data, and they have strong links to the theory of finance. In the context of finance, supervised learning models represent one of the most-used class of machine learning models. Regression algorithms, in contrast, estimate the outcome of problems that have an infinite number of solutions (continuous set of possible outcomes). Classification algorithms are probability-based, meaning the outcome is the category for which the algorithm finds the highest probability that the dataset belongs to it. Classification-based supervised learning methods identify which category a set of data items belongs to. Regression-based supervised learning methods try to predict outputs based on input variables. There are two varieties of supervised learning algorithms: regression and classification algorithms. In other words, supervised learning algorithms are provided with historical data and asked to find the relationship that has the best predictive power. Based on a massive set of data, the algorithm will learn a rule that it uses to predict the labels for new observations. A set of training data that contains labels is supplied to the algorithm. Supervised learning is an area of machine learning where the chosen algorithm tries to fit a target using the given input. ![]()
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