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Merge pull request #53 from JakeRadMSFT/u/jakerad/fix-github-notebook-display
Fix ML.NET Notebook Errors
2 parents 81da105 + bcc6b6f commit b3a8080

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‎machine-learning/04-Model Evaluation.ipynb‎

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@@ -204,7 +204,7 @@
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{
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"output_type": "execute_result",
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"data": {
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"text/html": "<table id=\"table_637927149236214714\"><thead><tr><th><i>index</i></th><th>vendor_id</th><th>rate_code</th><th>passenger_count</th><th>trip_time_in_secs</th><th>trip_distance</th><th>payment_type</th><th>fare_amount</th></tr></thead><tbody><tr><td><i><div class=\"dni-plaintext\">0</div></i></td><td>CMT</td><td><div class=\"dni-plaintext\">1</div></td><td><div class=\"dni-plaintext\">1</div></td><td><div class=\"dni-plaintext\">1271</div></td><td><div class=\"dni-plaintext\">3.8</div></td><td>CRD</td><td><div class=\"dni-plaintext\">17.5</div></td></tr><tr><td><i><div class=\"dni-plaintext\">1</div></i></td><td>CMT</td><td><div class=\"dni-plaintext\">1</div></td><td><div class=\"dni-plaintext\">1</div></td><td><div class=\"dni-plaintext\">474</div></td><td><div class=\"dni-plaintext\">1.5</div></td><td>CRD</td><td><div class=\"dni-plaintext\">8</div></td></tr><tr><td><i><div class=\"dni-plaintext\">2</div></i></td><td>CMT</td><td><div class=\"dni-plaintext\">1</div></td><td><div class=\"dni-plaintext\">1</div></td><td><div class=\"dni-plaintext\">637</div></td><td><div class=\"dni-plaintext\">1.4</div></td><td>CRD</td><td><div class=\"dni-plaintext\">8.5</div></td></tr><tr><td><i><div class=\"dni-plaintext\">3</div></i></td><td>CMT</td><td><div class=\"dni-plaintext\">1</div></td><td><div class=\"dni-plaintext\">1</div></td><td><div class=\"dni-plaintext\">181</div></td><td><div class=\"dni-plaintext\">0.6</div></td><td>CSH</td><td><div class=\"dni-plaintext\">4.5</div></td></tr><tr><td><i><div class=\"dni-plaintext\">4</div></i></td><td>CMT</td><td><div class=\"dni-plaintext\">1</div></td><td><div class=\"dni-plaintext\">1</div></td><td><div class=\"dni-plaintext\">661</div></td><td><div class=\"dni-plaintext\">1.1</div></td><td>CRD</td><td><div class=\"dni-plaintext\">8.5</div></td></tr></tbody></table>"
207+
"text/html": "<table id=\"table_637928803371228110\"><thead><tr><th><i>index</i></th><th>vendor_id</th><th>rate_code</th><th>passenger_count</th><th>trip_time_in_secs</th><th>trip_distance</th><th>payment_type</th><th>fare_amount</th></tr></thead><tbody><tr><td><i><div class=\"dni-plaintext\">0</div></i></td><td>CMT</td><td><div class=\"dni-plaintext\">1</div></td><td><div class=\"dni-plaintext\">1</div></td><td><div class=\"dni-plaintext\">1271</div></td><td><div class=\"dni-plaintext\">3.8</div></td><td>CRD</td><td><div class=\"dni-plaintext\">17.5</div></td></tr><tr><td><i><div class=\"dni-plaintext\">1</div></i></td><td>CMT</td><td><div class=\"dni-plaintext\">1</div></td><td><div class=\"dni-plaintext\">1</div></td><td><div class=\"dni-plaintext\">474</div></td><td><div class=\"dni-plaintext\">1.5</div></td><td>CRD</td><td><div class=\"dni-plaintext\">8</div></td></tr><tr><td><i><div class=\"dni-plaintext\">2</div></i></td><td>CMT</td><td><div class=\"dni-plaintext\">1</div></td><td><div class=\"dni-plaintext\">1</div></td><td><div class=\"dni-plaintext\">637</div></td><td><div class=\"dni-plaintext\">1.4</div></td><td>CRD</td><td><div class=\"dni-plaintext\">8.5</div></td></tr><tr><td><i><div class=\"dni-plaintext\">3</div></i></td><td>CMT</td><td><div class=\"dni-plaintext\">1</div></td><td><div class=\"dni-plaintext\">1</div></td><td><div class=\"dni-plaintext\">181</div></td><td><div class=\"dni-plaintext\">0.6</div></td><td>CSH</td><td><div class=\"dni-plaintext\">4.5</div></td></tr><tr><td><i><div class=\"dni-plaintext\">4</div></i></td><td>CMT</td><td><div class=\"dni-plaintext\">1</div></td><td><div class=\"dni-plaintext\">1</div></td><td><div class=\"dni-plaintext\">661</div></td><td><div class=\"dni-plaintext\">1.1</div></td><td>CRD</td><td><div class=\"dni-plaintext\">8.5</div></td></tr></tbody></table>"
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},
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"execution_count": 1,
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"metadata": {}
@@ -367,18 +367,9 @@
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}
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},
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"source": [
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"var result = await experiment.Run();"
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"var result = await experiment.RunAsync();"
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],
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"outputs": [
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{
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"output_type": "error",
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"ename": "",
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"evalue": "(1,14): error CS1061: 'TrialResult' does not contain a definition for 'GetAwaiter' and no accessible extension method 'GetAwaiter' accepting a first argument of type 'TrialResult' could be found (are you missing a using directive or an assembly reference?)",
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"traceback": [
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null
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]
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}
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]
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"outputs": []
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},
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{
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"cell_type": "markdown",
@@ -400,12 +391,12 @@
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],
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"outputs": [
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{
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"output_type": "error",
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"ename": "",
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"evalue": "(1,15): error CS0103: The name 'result' does not exist in the current context",
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"traceback": [
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null
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]
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"output_type": "execute_result",
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"data": {
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"text/plain": "R-Squared: 0.9329530384286037"
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},
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"execution_count": 1,
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"metadata": {}
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}
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]
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},
@@ -430,16 +421,7 @@
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"ITransformer bestModel = result.Model;\n",
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"var predictions = bestModel.Transform(testSet);"
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],
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"outputs": [
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{
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"output_type": "error",
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"ename": "",
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"evalue": "(1,26): error CS0103: The name 'result' does not exist in the current context",
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"traceback": [
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null
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]
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}
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]
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"outputs": []
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},
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{
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"cell_type": "markdown",
@@ -469,12 +451,12 @@
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],
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"outputs": [
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{
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"output_type": "error",
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"ename": "",
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"evalue": "(1,14): error CS0103: The name 'predictions' does not exist in the current context\r\n(2,17): error CS0103: The name 'predictions' does not exist in the current context",
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"traceback": [
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null
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]
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"output_type": "execute_result",
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"data": {
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"text/html": "<table><thead><tr><th><i>index</i></th><th>Actual</th><th>Predicted</th><th>Difference</th></tr></thead><tbody><tr><td>0</td><td><div class=\"dni-plaintext\">24.5</div></td><td><div class=\"dni-plaintext\">23.087162</div></td><td><div class=\"dni-plaintext\">1.412838</div></td></tr><tr><td>1</td><td><div class=\"dni-plaintext\">9.5</div></td><td><div class=\"dni-plaintext\">8.993666</div></td><td><div class=\"dni-plaintext\">0.5063343</div></td></tr><tr><td>2</td><td><div class=\"dni-plaintext\">4.5</div></td><td><div class=\"dni-plaintext\">4.808011</div></td><td><div class=\"dni-plaintext\">-0.30801105</div></td></tr><tr><td>3</td><td><div class=\"dni-plaintext\">8</div></td><td><div class=\"dni-plaintext\">7.994398</div></td><td><div class=\"dni-plaintext\">0.005601883</div></td></tr><tr><td>4</td><td><div class=\"dni-plaintext\">52</div></td><td><div class=\"dni-plaintext\">52.039684</div></td><td><div class=\"dni-plaintext\">-0.039684296</div></td></tr></tbody></table>"
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},
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"execution_count": 1,
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"metadata": {}
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}
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]
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},
@@ -487,19 +469,6 @@
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"With ML.NET, you don't have to manually calculate the evaluation metrics for your models. ML.NET provides a built-in `Evaluate` method for each of the machine learning tasks it supports. Use the `Evaluate` method for the regression task to calculate the evaluation metrics for the test set where the `fare_amount` column is the actual value and the `Score` column is the predicted value."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"dotnet_interactive": {
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"language": "csharp"
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}
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},
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"source": [
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""
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],
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"outputs": []
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},
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{
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"cell_type": "code",
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"execution_count": 1,
@@ -511,16 +480,7 @@
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"source": [
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"var evaluationMetrics = mlContext.Regression.Evaluate(predictions,\"fare_amount\", \"Score\");"
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],
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"outputs": [
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{
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"output_type": "error",
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"ename": "",
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"evalue": "(1,55): error CS0103: The name 'predictions' does not exist in the current context",
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"traceback": [
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null
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]
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}
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]
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"outputs": []
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},
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{
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"cell_type": "markdown",
@@ -542,12 +502,12 @@
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],
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"outputs": [
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{
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"output_type": "error",
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"ename": "",
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"evalue": "(1,1): error CS0103: The name 'evaluationMetrics' does not exist in the current context",
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"traceback": [
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null
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]
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"output_type": "execute_result",
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"data": {
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"text/html": "<table><thead><tr><th>MeanAbsoluteError</th><th>MeanSquaredError</th><th>RootMeanSquaredError</th><th>LossFunction</th><th>RSquared</th></tr></thead><tbody><tr><td><div class=\"dni-plaintext\">0.6107270253580241</div></td><td><div class=\"dni-plaintext\">6.673910566709432</div></td><td><div class=\"dni-plaintext\">2.58339129183123</div></td><td><div class=\"dni-plaintext\">6.673910534194763</div></td><td><div class=\"dni-plaintext\">0.9277130209892651</div></td></tr></tbody></table>"
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},
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"execution_count": 1,
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"metadata": {}
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}
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]
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},
@@ -961,7 +921,7 @@
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{
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"output_type": "execute_result",
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"data": {
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"text/html": "<table><thead><tr><th><i>index</i></th><th>Key</th><th>Value</th></tr></thead><tbody><tr><td>0</td><td>vendor_id.Bit2</td><td><div class=\"dni-plaintext\">-0.5100836529698106</div></td></tr><tr><td>1</td><td>vendor_id.Bit1</td><td><div class=\"dni-plaintext\">-0.2093168050110983</div></td></tr><tr><td>2</td><td>vendor_id.Bit0</td><td><div class=\"dni-plaintext\">-0.20509582275834345</div></td></tr><tr><td>3</td><td>payment_type.Bit3</td><td><div class=\"dni-plaintext\">-0.0014286018096295965</div></td></tr><tr><td>4</td><td>payment_type.Bit2</td><td><div class=\"dni-plaintext\">-0.0005391273248645708</div></td></tr><tr><td>5</td><td>payment_type.Bit1</td><td><div class=\"dni-plaintext\">-0.0001564149663926632</div></td></tr><tr><td>6</td><td>payment_type.Bit0</td><td><div class=\"dni-plaintext\">-7.393853265310242E-05</div></td></tr><tr><td>7</td><td>rate_code</td><td><div class=\"dni-plaintext\">-5.132793047627852E-07</div></td></tr><tr><td>8</td><td>passenger_count</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>9</td><td>trip_time_in_secs</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>10</td><td>trip_distance</td><td><div class=\"dni-plaintext\">0</div></td></tr></tbody></table>"
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"text/html": "<table><thead><tr><th><i>index</i></th><th>Key</th><th>Value</th></tr></thead><tbody><tr><td>0</td><td>vendor_id.Bit2</td><td><div class=\"dni-plaintext\">-0.5103167076996584</div></td></tr><tr><td>1</td><td>vendor_id.Bit1</td><td><div class=\"dni-plaintext\">-0.20920573710951015</div></td></tr><tr><td>2</td><td>vendor_id.Bit0</td><td><div class=\"dni-plaintext\">-0.20524726245559932</div></td></tr><tr><td>3</td><td>payment_type.Bit3</td><td><div class=\"dni-plaintext\">-0.0013735609832817113</div></td></tr><tr><td>4</td><td>payment_type.Bit2</td><td><div class=\"dni-plaintext\">-0.0005371983420188927</div></td></tr><tr><td>5</td><td>payment_type.Bit1</td><td><div class=\"dni-plaintext\">-0.00015402329111213753</div></td></tr><tr><td>6</td><td>payment_type.Bit0</td><td><div class=\"dni-plaintext\">-7.256291485776185E-05</div></td></tr><tr><td>7</td><td>rate_code</td><td><div class=\"dni-plaintext\">-5.605438192501921E-07</div></td></tr><tr><td>8</td><td>passenger_count</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>9</td><td>trip_time_in_secs</td><td><div class=\"dni-plaintext\">0</div></td></tr><tr><td>10</td><td>trip_distance</td><td><div class=\"dni-plaintext\">0</div></td></tr></tbody></table>"
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},
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"execution_count": 1,
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"metadata": {}

‎machine-learning/E2E-Classification with Iris Dataset.ipynb‎

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