I asked ChatGPT to: explain double machine learning for dummies Here is the answer: Double machine learning (DML) is a statistical method used to estimate treatment effects in the presence of high-dimensional controls. To put it simply, it’s a way to answer questions about cause and effect using data and machine learning, even when there […]
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Imports: import plotly.graph_objects as go Plot: fig = go.Figure() fig.add_trace(go.Scattermapbox( mode = "markers+lines", lon = [10, 20, 30], lat = [10, 15,30], marker = {'size': 10})) fig.add_trace(go.Scattermapbox( mode = "markers+lines", lon = [-50, -60,40], lat = [30, 10, -20], marker = {'size': 10})) fig.update_layout( margin ={'l':0,'t':0,'b':0,'r':0}, mapbox = { 'center': {'lon': 10, 'lat': 10}, 'style':
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Here is a plan to learn ML fundamentals in an afternoon by watching some videos on youtube: Follow this plan Machine learning fundamentals: What is Bias and Variance? – 6 minutes What is a Tensor? – 12 minutes [Stop and drink coffee, eat a snack] How to address bias and variance: Regularization (e.g. L2/Ridge) –
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Python has great options for plotting data. However, sometimes you want to explore data by changing parameters and rerunning plots to explore the effect of those changed parameters. This slows down the cycle of exploration. Luckily, Jupyter offers you a way to make you plots interactive, so you can see the effect of parameter changes
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