Month: October 2023
22nd Sept 2023
P-value:
- A measure that aids in assessing the significance of a specific finding in a statistical investigation is the p-value (probability value).
- It quantifies the evidence that refutes a null hypothesis. The null hypothesis frequently presupposes that the data are devoid of any influence or connection.
- A low p-value (usually less than 0.05) denotes statistical significance and shows substantial evidence opposing the null hypothesis.
- However, a high p-value indicates that there is little evidence to support the null hypothesis and that the result is not statistically significant.
R-squared:
- In regression analysis, the R-squared statistic is used to assess how well a model fits the data.
- It shows how much of the variance in the dependent variable, which is the variable being predicted, can be attributed to the model’s independent variables, or predictor variables.
- Higher numbers suggest a better fit, and R-squared values range from 0 to 1. An R-squared of 1 indicates that the model perfectly explains the data’s variance, whereas a value of 0 indicates that the model cannot account for any data’s variation.
- R-squared is used to measure how well a model fits observed data, although it is not always a reliable indicator of how well a model predicts the future.
2nd Oct 2023
Regularization is a technique that is used to prevent overfitting in predictive models. Overfitting occurs when a model learns to fit the training data too closely, capturing noise and making it perform poorly on new, unseen data. Regularization introduces a penalty term to the model’s loss function, discouraging it from learning overly complex patterns. Two common forms of regularization are L1 regularization (Lasso) and L2 regularization (Ridge), which add constraints to the model’s coefficients to reduce their magnitude. By doing so, regularization helps strike a balance between fitting the training data well and maintaining the model’s ability to generalize to new data, ultimately improving its performance on unseen examples.
29th Sept 2023
Mean Square Error (MSE) is a widely used mathematical metric in statistics and machine learning to measure the average squared difference between the values predicted by a model and the actual observed values in a dataset. To compute MSE, you take the difference between each predicted value and its corresponding actual value, square those differences to eliminate negative values, sum up all the squared differences, and then divide by the number of data points. A smaller MSE indicates that the model’s predictions are closer to the actual values, while a larger MSE suggests greater prediction errors. MSE is particularly useful for assessing the quality of regression models and quantifying their overall accuracy.