How to get Classification Report A classification report is a tool used in machine learning to evaluate the performance of a classification model. It is used to compare the predicted classes and the true classes of a dataset. The classification report in Python typically includes several metrics such as precision, recall, f1-score, and support. Precision …
Month: August 2019
Applied Data Science Coding with Python: How to get Classification LogLoss Metric LogLoss, also known as logarithmic loss, is a performance metric commonly used in classification problems. It measures the difference between predicted probability and the true label. In simple terms, it calculates the error rate between the predicted values and the actual values. The …
Applied Data Science Coding with Python: How to get Classification Confusion Matrix A confusion matrix is a table that is often used to describe the performance of a classification model on a set of test data for which the true values are known. The matrix is used to visualize the model’s predictions and compare them …
Applied Data Science Coding with Python: How to get Classification AUC ROC AUC-ROC (Area Under the Receiver Operating Characteristic curve) is a commonly used metric to evaluate the performance of a binary classification model. It is a graphical representation of the model’s ability to distinguish between the two classes, and it can be used to …
Applied Data Science Coding in Python: How to get Classification Accuracy Classification accuracy is a measure of how well a machine learning model is able to correctly predict the class of a given data point. In other words, it tells us what proportion of the predictions made by the model are correct. It is a …
Applied Data Science Coding in Python: Shuffle Split Cross Validation Shuffle Split Cross Validation (SSCV) is a method of evaluating the performance of a machine learning model. It is similar to other methods like k-fold cross-validation, but with a key difference: SSCV randomly splits the data into different training and test sets, rather than dividing …
Applied Data Science Coding in Python: Leave One Out Cross Validation Leave One Out Cross Validation (LOOCV) is a method of evaluating the performance of a machine learning model. It’s called “leave one out” because, in LOOCV, you train the model on all data points except for one, and then test the model on the …
Applied Data Science Coding in Python: How to prepare train test dataset Preparing a train and test dataset is an important step in the machine learning process. The train dataset is used to train the model, while the test dataset is used to evaluate the performance of the model. The goal is to train the …
Applied Data Science Coding in Python: Cross Validation Cross validation is a technique used in machine learning to evaluate the performance of a model. It is used to measure how well a model will perform on unseen data. The idea behind cross validation is to divide the data into several subsets, train the model on …
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