Bootstrap aggregation, or bagging, is an ensemble where each model is trained on a different sample of the training dataset. The idea of bagging can be generalized to other techniques for changing the training dataset and fitting the same model on each changed version of the data. One approach is to use data transforms that […]
Radius Neighbors Classifier is a classification machine learning algorithm. It is an extension to the k-nearest neighbors algorithm that makes predictions using all examples in the radius of a new example rather than the k-closest neighbors. As such, the radius-based approach to selecting neighbors is more appropriate for sparse data, preventing examples that are far […]
Hill climbing the test set is an approach to achieving good or perfect predictions on a machine learning competition without touching the training set or even developing a predictive model. As an approach to machine learning competitions, it is rightfully frowned upon, and most competition platforms impose limitations to prevent it, which is important. Nevertheless, […]
Many machine learning models perform better when input variables are carefully transformed or scaled prior to modeling. It is convenient, and therefore common, to apply the same data transforms, such as standardization and normalization, equally to all input variables. This can achieve good results on many problems. Nevertheless, better results may be achieved by carefully […]
Machine learning predictive modeling performance is only as good as your data, and your data is only as good as the way you prepare it for modeling. The most common approach to data preparation is to study a dataset and review the expectations of a machine learning algorithms, then carefully choose the most appropriate data […]
There are a vast number of different types of data preparation techniques that could be used on a predictive modeling project. In some cases, the distribution of the data or the requirements of a machine learning model may suggest the data preparation needed, although this is rarely the case given the complexity and high-dimensionality of […]
Machine learning predictive modeling performance is only as good as your data, and your data is only as good as the way you prepare it for modeling. The most common approach to data preparation is to study a dataset and review the expectations of a machine learning algorithm, then carefully choose the most appropriate data […]
Data Preparation for Machine Learning Crash Course. Get on top of data preparation with Python in 7 days. Data preparation involves transforming raw data into a form that is more appropriate for modeling. Preparing data may be the most important part of a predictive modeling project and the most time-consuming, although it seems to be […]
Data preparation is the process of transforming raw data into a form that is appropriate for modeling. A naive approach to preparing data applies the transform on the entire dataset before evaluating the performance of the model. This results in a problem referred to as data leakage, where knowledge of the hold-out test set leaks […]
Machine learning algorithms like Linear Regression and Gaussian Naive Bayes assume the numerical variables have a Gaussian probability distribution. Your data may not have a Gaussian distribution and instead may have a Gaussian-like distribution (e.g. nearly Gaussian but with outliers or a skew) or a totally different distribution (e.g. exponential). As such, you may be […]
