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Log loss, also known as logistic regression loss or cross-entropy loss, is a performance metric used to evaluate the performance of classification models. It measures the difference between the predicted probabilities of a model and the true labels of the data. Log loss is commonly used in binary classification problems, where the goal is to […]
AUC-ROC (Area Under the Receiver Operating Characteristic Curve) is a performance metric that evaluates the ability of a classification model to distinguish between classes. It is a widely used metric in machine learning, particularly in medical diagnosis and fraud detection. The ROC curve is a graphical representation of the trade-off between the true positive rate […]
Perplexity is a measure of the difficulty of a language model to predict the next word in a sequence. It is often used to evaluate the performance of a language model and to compare different models. Perplexity is calculated by taking the exponentiation of the negative average log-likelihood of the data. The average log-likelihood is […]
Kullback-Leibler (KL) divergence, also known as relative entropy, is a measure of how different two probability distributions are. It is often used to compare the predicted distribution of a model to the true distribution of the data. KL divergence is also used in information theory, where it is a measure of the information lost when […]
Cross entropy is a measure of the difference between two probability distributions. It is often used in machine learning to measure the performance of a model. A lower cross entropy means that the model is making better predictions. Cross entropy is calculated using the following formula:
Mutual information is a measure of the dependency between two random variables. It quantifies the amount of information that one variable contains about another. Mutual information is widely used in various fields, such as information theory, statistics, machine learning, and data analysis. Mutual information provides several key benefits. Firstly, it measures the strength of the […]
Within the field of machine learning, a key concept known as information gain plays a pivotal role in decision tree learning. Information gain quantifies the reduction in entropy, or uncertainty, when a dataset is split based on a particular attribute. By harnessing this metric, machine learning algorithms can make informed decisions about which attributes to […]
The Gini index is a measure of statistical dispersion intended to represent the income or wealth distribution of a nation’s residents. It is most commonly used as a measure of inequality. The Gini index is calculated by dividing the area between the Lorenz curve and the line of perfect equality by the area under the […]
Entropy is a measure of disorder or randomness in a system. It is often used to describe the state of a system in thermodynamics, information theory, and statistical mechanics. In thermodynamics, entropy is a measure of the thermal energy of a system that is unavailable for work. In information theory, entropy is a measure of […]
Gain, in the financial world, refers to the profit or positive return on an investment. It is calculated by subtracting the initial cost of the investment from its current value. For instance, if an investor purchases a stock for $100 and sells it later for $120, their gain is $20. Calculating gain is crucial for […]