Cross-validation is a statistical technique used in mathematical statistics and machine learning to assess the performance and generalization ability of a predictive model. It involves partitioning a dataset into multiple subsets, training the model on some of these subsets, and then evaluating its performance on the remaining data . The primary goal of cross-validation is to estimate how well the model will perform on unseen data, which helps in model selection and hyperparameter tuning. Cross-validation helps in model selection by comparing the performance of different models on the same dataset. It also aids in hyperparameter tuning by assessing how different hyperparameters affect model performance. By using cross-validation, researchers and data scientists can make more informed decisions about which models and parameter settings are likely to perform well when applied to new, unseen data, thereby improving the reliability of statistical analyses and predictions.
25th Sept – Monday
A key idea in machine learning and statistical modeling is “Cross-Validation: The Right and Wrong Ways”. The performance and generalizability of predictive models are evaluated using the cross-validation technique. It is likely that both proper and improper cross-validation procedures are covered in this video or topic. The “right way” probably entails carefully dividing the data into training and validation sets, choosing the optimal number of folds (for example, 5 or 10), and thoroughly assessing a model’s performance to prevent overfitting or underfitting. Contrarily, employing the “wrong way” could entail making typical errors like data leaking, using the incorrect assessment metrics, or treating imbalanced datasets improperly. Building trustworthy and dependable machine learning models requires an understanding of these correct and incorrect methods.
22nd September, Friday
K-nearest neighbors (KNN) is a simple and popular machine learning algorithm used for classification and regression tasks. Here’s a brief overview:
KNN is based on the idea that similar data points tend to be close to each other in the feature space. It makes predictions by finding the K data points in the training set that are closest (most similar) to a given input data point.
Parameters: The main parameter in KNN is ‘K,’ which represents the number of nearest neighbors to consider when making a prediction. A smaller K value makes the algorithm more sensitive to noise, while a larger K value makes it smoother but might miss fine-grained patterns.
We used KNN in our project, by using it we got r^2 = 0.23, which is less than linear regression.
20th September,Wednesday
In today’s class we have discussed about Crab Molt Model , A crab molt model in statistical analysis typically refers to a statistical model used to study and analyze the molting behavior of crabs. Molting is a natural process in which crabs shed their old exoskeleton and grow a new one. Understanding the timing and frequency of molting in crab populations is important for various ecological and fisheries management purposes. Statistical models can help researchers analyze and predict molt patterns based on various factors and variables.
In summary, a crab molt model in statistical analysis is a tool used to study and quantify the molting behavior of crabs, with the goal of understanding the factors that influence this behavior. These models can help researchers and practitioners make informed decisions related to crab populations and management.
Today in class we discussed about linear regression model with more than one predictor variable, known as multiple linear regression, aims to capture the relationship between a dependent variable and several independent predictor variables. In this model, the dependent variable is expressed as a linear combination of these predictors, with each predictor having its own coefficient that signifies the strength and direction of its impact on the dependent variable. The model allows us to assess how changes in each predictor, while holding others constant, influence the outcome. By estimating these coefficients through techniques like least squares, we can make predictions, understand the significance of each predictor, and assess the overall goodness of fit. Multiple linear regression is a valuable tool in fields like economics, science, and social sciences for uncovering complex relationships and making data-driven predictions.
September 15, Friday
Today we have learned about linear regression model in the class and topics related to it.
Linear regression is a widely used statistical method for modeling the relationship between a dependent variable and one or more independent variables. It assumes a linear relationship between the predictors and the target variable. The main goal of linear regression is to find the best-fitting linear equation that describes this relationship.
Simple Linear Regression: In simple linear regression, there is one independent variable and one dependent variable (target). The relationship between them is modeled as a straight line.
Multiple Linear Regression: In multiple linear regression, there are two or more independent variables and one dependent variable (target). The relationship is modeled as a linear combination of the predictors:
September 13th, Wednesday
In today’s class, we discussed about
Null hypothesis, p-value, and the Breusch-Pagan Test are fundamental elements in statistical analysis, particularly in regression studies. The null hypothesis serves as a starting point, positing that there is no significant variation in the error term’s variance across independent variables. Meanwhile, the p-value quantifies the strength of evidence against this null hypothesis. A low p-value (typically < 0.05) indicates evidence to reject the null hypothesis, signifying the presence of heteroskedasticity, while a high p-value suggests constant variance. The Breusch-Pagan Test specifically assesses conditional heteroskedasticity, revealing if the error variance is linked to independent variables.
Hypotheses for heteroskedasticity are at the core of this analysis. The null hypothesis assumes no heteroskedasticity, implying consistent error variance across independent variables. In contrast, the alternative hypothesis suggests the presence of heteroskedasticity, indicating variable error variance. These hypotheses guide researchers in understanding the nature of variability in their regression models. Overall, comprehending these concepts is vital for assessing and addressing heteroskedasticity, ensuring the reliability of statistical models.
September 11th, Monday
In the today’s lecture we have discussed regarding the concept’s linear regression and the concepts related to work on dataset CDC Diabetes 2018.
It is a fundamental statistical method used to model the relationship between a dependent variable and one or more independent variables. It is a powerful tool for understanding and predicting how changes in the independent variables affect the dependent variable.
To study dataset given i.e., CDC Diabetes 2018, we have discussed regarding some statistical methods Median, Standard Deviation, Skewness, Kurtosis. The Dataset consists of three variables obesity, inactivity and
diabetes and There are 354 rows of data that contain information on all 3
variables. Generated a description of the %diabetes,
and inactivity data for these 1370 common data points. By this step in statistical analysis, we can understand and analyze the data.
Skewness at 0.658616 suggests that our data is slightly skewed. Imagine the data as a bell curve – if it’s perfectly symmetrical, the skewness would be 0. Positive skewness means the data is stretched out to the right. A kurtosis of about 4 indicates that our data is somewhat more peaked and has heavier tails compared to a normal distribution (which has a kurtosis of 3). This means it has more extreme values.
These concepts were discussed in the class, and they help to gain more insights in statistical analysis and to solve these CDC Diabetes 2018 kind of datasets.
Hello world!
Welcome to UMassD WordPress. This is your first post. Edit or delete it, then start blogging!