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172 | 172 | "collapsed": true |
173 | 173 | }, |
174 | 174 | "source": [ |
175 | | - "<p style=\"font-family:verdana; font-size:15px\"><b> Support Vector Machines</b> are supervised learning models which can be used for both classification and regression. SVMs are among the best supervised learning algorithms. It is effective in high dimensional space and it is memory efficient as well.\n", |
| 175 | + "<p style=\"font-family:verdana; font-size:15px\" id=\"svm\"><b> Support Vector Machines</b> are supervised learning models which can be used for both classification and regression. SVMs are among the best supervised learning algorithms. It is effective in high dimensional space and it is memory efficient as well.\n", |
176 | 176 | "\n", |
177 | 177 | "Consider a binary classification problem, where the task is to assign a one of the two labels to given input. We plot each data item as a point in n-dimensional space as follows:</p>\n", |
178 | 178 | "\n", |
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834 | 834 | "cell_type": "markdown", |
835 | 835 | "metadata": {}, |
836 | 836 | "source": [ |
837 | | - "<p style=\"font-family:verdana; font-size:15px\">Decision Tree is the supervised learning algorithm which can be used for classification as well as regression problems. Decision Tree is very popular learning algorithm because of its interpretability. In this method, we split population into set of homogeneous sets by asking set of questions. Consider a problem where we want to decide what to do on a particular day. We can design a decision tree as follows : (Source: Python Machine Learning by Sebastian Raschka)</p>\n", |
| 837 | + "<p style=\"font-family:verdana; font-size:15px\" id=\"dt\">Decision Tree is the supervised learning algorithm which can be used for classification as well as regression problems. Decision Tree is very popular learning algorithm because of its interpretability. In this method, we split population into set of homogeneous sets by asking set of questions. Consider a problem where we want to decide what to do on a particular day. We can design a decision tree as follows : (Source: Python Machine Learning by Sebastian Raschka)</p>\n", |
838 | 838 | "" |
839 | 839 | ] |
840 | 840 | }, |
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1042 | 1042 | "cell_type": "markdown", |
1043 | 1043 | "metadata": {}, |
1044 | 1044 | "source": [ |
1045 | | - "<p style=\"font-family:verdana; font-size:15px\">\n", |
| 1045 | + "<p style=\"font-family:verdana; font-size:15px\" id=\"ensemble\">\n", |
1046 | 1046 | "Till now, we discussed methods in which there was only one hypothesis. In other words, we were training only one model for one problem. In ensemble learning, we train a set of models to achieve better accuracy and stability. At prediction time we combine the prediction of all. In case of classification, we take vote of each model and in case of regression, we take the mean of the prediction of each model. We run multiple models on the data and use the aggregate prediction, which is better than a single model.<br><br></p>\n", |
1047 | 1047 | "<p style=\"font-family:verdana; font-size:15px\">\n", |
1048 | 1048 | "<b> Bagging (Bootstrap aggregating)</b> is a technique used to reduce the variance of a prediction by combining the result of multiple classifiers. In baggin, first we create multiple datasets from the original dataset. Please note, these datasets have random number of samples and random number of features. Then we build classifier on each dataset. For prediction, we use combine predictions of models.<br><br>\n" |
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1267 | 1267 | "cell_type": "markdown", |
1268 | 1268 | "metadata": {}, |
1269 | 1269 | "source": [ |
1270 | | - "<p style=\"font-family:verdana; font-size:15px\">\n", |
| 1270 | + "<p style=\"font-family:verdana; font-size:15px\" id=\"ex\">\n", |
1271 | 1271 | "In <a href=\"Sentiment%20Analysis.ipynb\">this</a> exercise, we will implement a sentiment analysis model which can detect the sentiment from a text. We will also go through some feature extraction techniques and learn how to use textual data in machine learning models." |
1272 | 1272 | ] |
| 1273 | + }, |
| 1274 | + { |
| 1275 | + "cell_type": "code", |
| 1276 | + "execution_count": null, |
| 1277 | + "metadata": { |
| 1278 | + "collapsed": true |
| 1279 | + }, |
| 1280 | + "outputs": [], |
| 1281 | + "source": [] |
1273 | 1282 | } |
1274 | 1283 | ], |
1275 | 1284 | "metadata": { |
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