1 00:00:00,050 --> 00:00:00,560 Lesson. 2 00:00:00,560 --> 00:00:02,780 Feature engineering for AI models. 3 00:00:02,810 --> 00:00:09,050 Feature engineering is an indispensable step in the AI development lifecycle, specifically within the 4 00:00:09,050 --> 00:00:11,270 stages of development and testing. 5 00:00:11,720 --> 00:00:17,570 It involves the process of using domain knowledge to extract features from raw data, which can lead 6 00:00:17,600 --> 00:00:19,460 to improved model performance. 7 00:00:20,210 --> 00:00:26,000 This phase demands a deep understanding of the data and the problem at hand, as well as creativity 8 00:00:26,000 --> 00:00:27,320 and technical skills. 9 00:00:27,320 --> 00:00:32,930 The quality of the features derived directly impacts the effectiveness of the machine learning models, 10 00:00:32,930 --> 00:00:37,880 making feature engineering a critical skill for any AI governance professional. 11 00:00:39,500 --> 00:00:45,620 The goal of feature engineering is to transform raw data into meaningful features that capture the underlying 12 00:00:45,620 --> 00:00:48,260 patterns necessary for predictive modeling. 13 00:00:48,830 --> 00:00:55,190 This process typically involves several techniques, including extraction, transformation, and creation 14 00:00:55,190 --> 00:00:56,480 of new features. 15 00:00:57,260 --> 00:01:02,440 For instance, in a dataset containing timestamps, one might extract features such as the day of the 16 00:01:02,440 --> 00:01:06,520 week, time of day, or whether the time stamp falls on a holiday. 17 00:01:06,520 --> 00:01:11,830 Such transformations can reveal significant patterns that were not apparent in the raw data. 18 00:01:13,360 --> 00:01:18,310 One key aspect of feature engineering is the identification of relevant features. 19 00:01:18,340 --> 00:01:24,340 Irrelevant or redundant features can introduce noise and reduce the model's ability to generalize. 20 00:01:24,910 --> 00:01:27,940 This is where domain expertise plays a crucial role. 21 00:01:28,420 --> 00:01:34,300 Understanding the context and nuances of the data helps in selecting features that genuinely contribute 22 00:01:34,300 --> 00:01:36,370 to the predictive power of the model. 23 00:01:36,820 --> 00:01:42,760 For example, in a healthcare data set, domain knowledge might suggest that age, blood pressure, 24 00:01:42,760 --> 00:01:48,400 and cholesterol levels are important predictors of heart disease, whereas patient ID numbers are not. 25 00:01:49,960 --> 00:01:53,980 Statistical methods are often employed to assess the relevance of features. 26 00:01:54,430 --> 00:02:00,370 Techniques such as correlation analysis, mutual information, and principal component analysis can 27 00:02:00,430 --> 00:02:03,490 help identify which features are most informative. 28 00:02:04,060 --> 00:02:09,820 Correlation analysis, for example, measures the linear relationship between features and the target 29 00:02:09,820 --> 00:02:10,570 variable. 30 00:02:11,170 --> 00:02:16,240 Features with high correlation to the target and low correlation with each other are typically more 31 00:02:16,240 --> 00:02:17,020 valuable. 32 00:02:17,380 --> 00:02:24,460 PCA, on the other hand, reduces dimensionality by transforming features into a set of orthogonal components, 33 00:02:24,460 --> 00:02:26,950 retaining as much variance as possible. 34 00:02:27,970 --> 00:02:31,360 Feature scaling is another critical step in feature engineering. 35 00:02:31,690 --> 00:02:37,390 Many machine learning algorithms, such as gradient descent based methods, are sensitive to the scale 36 00:02:37,390 --> 00:02:38,320 of the features. 37 00:02:38,920 --> 00:02:44,560 Techniques like normalization and standardization are used to bring all features to a similar scale. 38 00:02:45,550 --> 00:02:52,300 Normalization typically scales features to a range between 0 and 1, while standardization transforms 39 00:02:52,300 --> 00:02:56,230 features to have a mean of zero and a standard deviation of one. 40 00:02:56,230 --> 00:03:02,210 This ensures that each feature contributes equally to the model and prevents features with larger scales 41 00:03:02,210 --> 00:03:04,400 from dominating the learning process. 42 00:03:05,870 --> 00:03:10,880 Handling missing values and outliers is also a vital part of feature engineering. 43 00:03:11,480 --> 00:03:15,950 Missing values can distort the training process and lead to biased models. 44 00:03:16,700 --> 00:03:22,730 Strategies for handling missing data include imputation, where missing values are replaced with statistical 45 00:03:22,730 --> 00:03:29,120 estimates such as the mean, median, or mode, and more sophisticated techniques like k nearest neighbors. 46 00:03:29,120 --> 00:03:29,990 Imputation. 47 00:03:30,410 --> 00:03:35,780 Outliers, which are data points significantly different from the majority of the data, can skew the 48 00:03:35,780 --> 00:03:36,410 model. 49 00:03:36,650 --> 00:03:41,840 Methods to handle outliers include removing them or transforming them using techniques such as Windsor 50 00:03:41,840 --> 00:03:45,650 ization, where outliers are capped at a specified percentile. 51 00:03:47,390 --> 00:03:53,270 Feature construction or the creation of new features can significantly enhance model performance. 52 00:03:53,600 --> 00:03:58,970 This process involves generating new features from existing ones through mathematical transformations, 53 00:03:58,970 --> 00:04:02,040 aggregations, or domain specific knowledge. 54 00:04:02,070 --> 00:04:08,070 For instance, in time series data lag features, which are values from previous time steps, can be 55 00:04:08,070 --> 00:04:12,300 constructed to capture temporal dependencies in text data. 56 00:04:12,300 --> 00:04:14,010 Features like term frequency. 57 00:04:14,010 --> 00:04:19,440 Inverse document frequency can be created to represent the importance of words in a document relative 58 00:04:19,440 --> 00:04:20,460 to a corpus. 59 00:04:22,320 --> 00:04:27,810 Automated feature engineering tools such as feature tools and auto fit have emerged to streamline the 60 00:04:27,810 --> 00:04:28,740 process. 61 00:04:29,010 --> 00:04:34,830 These tools leverage algorithms to automatically generate and select features, reducing the manual 62 00:04:34,830 --> 00:04:35,970 effort involved. 63 00:04:36,690 --> 00:04:42,600 However, while these tools can be powerful, they are not a substitute for domain knowledge and expertise. 64 00:04:43,140 --> 00:04:48,660 The best results are often achieved through a combination of automated tools and manual feature engineering. 65 00:04:50,730 --> 00:04:55,560 Effective feature engineering also involves iterative experimentation and validation. 66 00:04:55,890 --> 00:05:02,170 The process is inherently iterative, requiring continuous Experimentation and validation to refine 67 00:05:02,170 --> 00:05:03,070 the features. 68 00:05:03,640 --> 00:05:08,770 Cross validation techniques are used to assess the performance of the features and the model. 69 00:05:08,950 --> 00:05:14,830 This involves dividing the data into training and validation sets multiple times and evaluating the 70 00:05:14,830 --> 00:05:17,020 model's performance on each split. 71 00:05:17,530 --> 00:05:23,530 Techniques such as k-fold cross validation help ensure that the model is not overfitting, and can generalize 72 00:05:23,530 --> 00:05:25,180 well to unseen data. 73 00:05:26,620 --> 00:05:32,500 Real world examples of successful feature engineering highlight its importance in the famous Netflix 74 00:05:32,500 --> 00:05:33,640 Prize competition. 75 00:05:33,640 --> 00:05:39,370 The winning team improved their recommendation systems performance by ingeniously engineering features 76 00:05:39,370 --> 00:05:41,740 from user ratings and movie metadata. 77 00:05:42,160 --> 00:05:48,250 They created features capturing temporal dynamics such as user preferences changing over time, which 78 00:05:48,250 --> 00:05:50,770 significantly boosted the model's accuracy. 79 00:05:52,990 --> 00:05:59,050 Moreover, feature engineering is not a one time task, but a continuous process throughout the AI development 80 00:05:59,050 --> 00:06:00,060 life cycle. 81 00:06:00,060 --> 00:06:05,130 As new data becomes available, features may need to be reevaluated and updated. 82 00:06:05,160 --> 00:06:11,310 Additionally, as the problem domain evolves, new features may become relevant, necessitating ongoing 83 00:06:11,340 --> 00:06:12,900 feature engineering efforts. 84 00:06:14,160 --> 00:06:20,820 In conclusion, feature engineering is a critical component of the AI development life cycle that significantly 85 00:06:20,820 --> 00:06:22,710 influences model performance. 86 00:06:22,920 --> 00:06:29,010 It involves the extraction, transformation, and creation of features from raw data, leveraging domain 87 00:06:29,010 --> 00:06:31,350 knowledge and statistical techniques. 88 00:06:32,010 --> 00:06:37,470 Effective feature engineering requires careful selection of relevant features, handling of missing 89 00:06:37,470 --> 00:06:41,850 values and outliers, and iterative experimentation and validation. 90 00:06:41,880 --> 00:06:46,980 Automated tools can aid in this process, but domain expertise remains crucial. 91 00:06:47,730 --> 00:06:53,220 Successful feature engineering, as demonstrated by real world examples, can lead to substantial improvements 92 00:06:53,220 --> 00:06:59,100 in model accuracy and generalizability, underscoring its importance for AI governance professionals.