5/29/2023 0 Comments Logistic regression r studio![]() Projects under the domain of Agri tech, finance, stocks exchange, business, manufacturing, transportation and health care that will boost your business I have experience in building predictive models, clustering algorithms, and recommender systemsĪI Projects: Any AI, machine learning or deep learning project you have in mindīuild Supervised and unsupervised machine learning models including classification, regression, and clustering models (Linear Regression, Logistic Regression,Decision Tree,SVM, KNN,K-Means,Random Forest, etc)ĭeep learning models including various architectures like CNN, RNN, and Transformers NLTK, SpaCy, ChatGPT, BERT, other NLP toolsĭata Science: I am proficient in data analysis, data visualization, and machine learning. Natural Language Processing (NLP): Chatbot, sentiment analysis, text classification, CV ranking. I am proficient in OpenCV, TensorFlow, PyTorch, and other tools. If you are looking for guidance or complete project under the below domains.Ĭomputer Vision: Object detection, image segmentation, and facial recognition etc. I have published many research papers, and you can find me easily (google scholar, semantic scholar, research gate) Interpretation of Standardized CoefficientĪ standardized coefficient value of 2.5 explains one standard deviation increase in independent variable on average, a 2.5 standard deviation increase in the log odds of dependent variable.I am a PhD student in Artificial intelligence and Data Science with 5+ years of experience in various Machine Learning fields. ![]() The most important variable will have maximum absolute value of standardized coefficient. We can rank independent variables with absolute value of standardized coefficients. Standardized Coefficients (or Estimates) are mainly used to rank predictors (or independent or explanatory variables) as it eliminate the units of measurement of independent and dependent variables). If we need to rank these predictors based on the unstandardized coefficient, it would not be a fair comparison as the unit of these variable is not same. The variable 'age' is expressed in years, height in cm, weight in kg. Suppose you have 3 independent variables - age, height and weight. The concept of standardization or standardized coefficients (aka estimates) comes into picture when predictors (aka independent variables) are expressed in different units. ![]() Odd Ratio (exp of estimate) less than 1 => Negative relationship (It means negative coefficient value of estimate coefficients) For example, if Exp(B) = 2 on a positive effect variable, this has the same magnitude as variable with Exp(B) = 0.5 = ½ but in the opposite direction. Magnitude : If you want to compare the magnitudes of positive and negative effects, simply take the inverse of the negative effects. Note : To calculate 5 unit increase, 4.27 ^ 5 (instead of multiplication). The odds of a person having years of experience getting a job are 4.27 times greater than the odds of a person having no experience. In other words, the odds of getting a job are increased by 327% (4.27-1)*100 for an unit increase in years of experience. It is exponential value of estimate.Īn unit increase in years of experience increases the odds of getting a job by a multiplicative factor of 4.27, given the other variables in the model are held constant. In logistic regression, the odds ratio is easier to interpret. Fisher Scoring is the most popular iterative method of estimating the regression parameters. With ML, the computer uses different "iterations" in which it tries different solutions until it gets the maximum likelihood estimates. Logistic regression is based on Maximum Likelihood (ML) Estimation which says coefficients should be chosen in such a way that it maximizes the Probability of Y given X (likelihood). In this case, two possible categories in dependent variable : " High Blood Pressure" and " Normal Blood Pressure". We are interested in knowing how variables, such as age, sex, body mass index, effect blood pressure (sbp). In this case, two possible categories in dependent variable : " Promoted" and " Not Promoted".Ģ. We are trying to calculate the factors that affects promotion. An employee may get promoted or not based on age, years of experience, last performance rating etc. In other words, it is multiple regression analysis but with a dependent variable is categorical.ġ. It is used to predict the result of a categorical dependent variable based on one or more continuous or categorical independent variables.
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