Supervised learning in Machine Learning.pptx

Machine learning

1. UNIT – 2 SUPERVISED LEARNING

2. agenda Multivariate Regression 2

3. MULTIVARIATE REGRESSION Multivariate regression is a powerful statistical technique that analyzes the relationship between a dependent variable and multiple independent variables. But Multivariate Regression deals with one dependent variable and multiple independent variables . In multivariate regression, we aim to predict an output based on several independent variables. It's widely used in various fields, including economics, finance, and healthcare, to understand complex relationships and make accurate predictions. 3

4. NON LINEAR REGRESSION AND MULTIVARIATE REGRESSION- THE DIFFERENCE 4

5. EXAMPLE: 5

6. LOGISTIC REGRESSION The LOGISTIC REGRESSION model transforms the linear regression’s continuous value output into categorical value output using a sigmoid function, which maps any real-valued set of independent variables input into a value between 0 and 1. This function is known as the logistic function. 6

7. LOGISTIC REGRESSION… Let the independent input features be: X = [x11 …x1m] [x21 …x2m]  [xn1 … xnm ]   and the dependent variable is Y  0 or 1.  then, apply the multi-linear function to the input variables X. Here  xi​  is the ith observation of X. wi =[w1,w2,w3,⋯,wm] , here w  is the weights or Coefficient, and b is the bias term also known as intercept, simply this can be represented as the dot product of weight and bias. 7

8. TYPES - LOGISTIC REGRESSION Binomial:  In binomial Logistic regression, there can be only two possible types of the dependent variables, such as 0 or 1, Pass or Fail, etc. Multinomial:  In multinomial Logistic regression, there can be 3 or more possible unordered types of the dependent variable, such as “cat”, “dogs”, or “sheep” Ordinal:  In ordinal Logistic regression, there can be 3 or more possible ordered types of dependent variables, such as “low”, “Medium”, or “High”. 8

9. LOGISTIC REGRESSION… # import the necessary libraries from sklearn.datasets import load_breakfast_items from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score X, y = load_ breakfast_items ( return_X_y =True) # split the train and test dataset X_train , X_test ,\ y_train , y_test = train_test_split (X, y, test_size =0.20, random_state =23) # LogisticRegression clf = LogisticRegression ( random_state =0) clf.fit ( X_train , y_train ) # Prediction y_pred = clf.predict ( X_test ) acc = accuracy_score ( y_test , y_pred ) print("Logistic Regression model accuracy (in %):", acc *100) OUTPUT: Logistic Regression model accuracy (in %): 95.6140350877193 9

10. Effective delivery techniques 10 This is a powerful tool in public speaking. It involves varying pitch, tone, and volume to convey emotion, emphasize points, and maintain interest. Pitch variation Tone inflection Volume control Effective body language enhances your message, making it more impactful and memorable. Meaningful eye contact Purposeful gestures Maintain good posture Control your expressions

11. Navigating Q&A sessions Maintaining composure during the Q&A session is essential for projecting confidence and authority. Consider the following tips for staying composed: Stay calm Actively listen Pause and reflect Maintain eye contact Know your material in advance Anticipate common questions Rehearse your responses 11

12. Speaking impact Your ability to communicate effectively will leave a lasting impact on your audience Effectively communicating involves not only delivering a message but also resonating with the experiences, values, and emotions of those listening  12

13. Dynamic delivery Learn to infuse energy into your delivery to leave a lasting impression One of the goals of effective communication is to motivate your audience Metric Measurement Target Actual Audience attendance # of attendees 150 120 Engagement duration Minutes 60 75 Q&A interaction # of questions 10 15 Positive feedback Percentage (%) 90 95 Rate of information retention Percentage (%) 80 85 13

14. Final tips & takeaways Consistent rehearsal Strengthen your familiarity Refine delivery style Pacing, tone, and emphasis Timing and transitions Aim for seamless, professional delivery Practice audience Enlist colleagues to listen & provide feedback Seek feedback Reflect on performance Explore new techniques Set personal goals Iterate and adapt 14

15. Speaking engagement metrics Impact factor Measurement Target Achieved Audience interaction Percentage (%) 85 88 Knowledge retention Percentage (%) 75 80 Post-presentation surveys Average rating 4.2 4.5 Referral rate Percentage (%) 10 12 Collaboration opportunities # of opportunities 8 10 15

16. Thank you Brita Tamm 502-555-0152 [email protected] www.firstupconsultants.com